Lab 2 : AssurExperts INC

Travail elaboré par :
Mohamed Yassine Hakimi
Iheb Jridi
Baha Khayatti
Amal Chaari
Ameni Lefi

Compréhension métier

Les entreprises utilisent l’e-mailing comme outil de marketing direct et unidirectionnel pour informer sur leur actualité, leurs nouveautés,leurs services ou autres. Les e-mailings s’inscrivent ainsi dans une campagne marketing de l’entreprise qui vise à renforcer un message donné. En général, les destinataires des campagnes d’e-mailings sont les clients et ont donc de fortes probabilités d’être intéressés par les services et/ou produits. En revanche, un envoi abusif peut exemplifier un dérangement des clients, qui deviennent progressivement hostiles ou désintéressés à chaque réception.

Pour ce faire nous avons:

1- decrit les différents comportements des clients .

2- Mettre en place une modélisation qui servira à maximiser le nombre des clients réceptifs en ciblant au plus la moitié de la clientèle active.

3- ciblé la clientèle active et receptif aux offres de la compagnie.

Compréhension des données

In [1]:
import numpy as np
import pandas as pd
import plotly.plotly as py
import plotly.offline as pf
from plotly.offline import init_notebook_mode 
import plotly.graph_objs as go
import plotly.figure_factory as ff
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot

import plotly.figure_factory as F
import seaborn as sns
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_predict
from sklearn.metrics import classification_report
from sklearn.feature_selection import RFE, f_regression
from sklearn.linear_model import (LinearRegression, Ridge, Lasso, RandomizedLasso)
import xgboost as xgb
from lightgbm import LGBMClassifier
from sklearn.svm import SVC
from sklearn.model_selection import cross_validate
from sklearn.metrics import roc_curve, auc
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestRegressor
from sklearn.tree import DecisionTreeClassifier
import pandas as pd 
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.cluster import KMeans

from mlxtend.frequent_patterns import apriori
from mlxtend.frequent_patterns import association_rules
In [4]:
import plotly
plotly.tools.set_credentials_file(username='iheb.jridi', api_key='6MSvFnd4zja3zqQlzjqb')
In [5]:
init_notebook_mode()

Importation des données:

In [8]:
data = pd.read_table('C:/Users/AMAL/Desktop/1 SEM/ML/LAB 2/AssurancExpertsInc.txt')
In [9]:
data.head()
Out[9]:
SD1 SD2 SD3 SD4 SD5 SD6 SD7 SD8 SD9 SD10 ... PO78 PO79 PO80 PO81 PO82 PO83 PO84 PO85 CLASS STATUS
0 33 1 3 2 8 0 5 1 3 7 ... 0 0 1 0 0 0 0 0 No Learning
1 37 1 2 2 8 1 4 1 4 6 ... 0 0 1 0 0 0 0 0 No Learning
2 37 1 2 2 8 0 4 2 4 3 ... 0 0 1 0 0 0 0 0 No Learning
3 9 1 3 3 3 2 3 2 4 5 ... 0 0 1 0 0 0 0 0 No Learning
4 40 1 4 2 10 1 4 1 4 7 ... 0 0 1 0 0 0 0 0 No Learning

5 rows × 87 columns

Ce dataset comporte 9822 individus. c'est un échantillon de données représentatif décrivant 9822 clients actifs chez AssurExperts Inc durant les vingt dernières années

In [6]:
data.describe()
Out[6]:
SD1 SD2 SD3 SD4 SD5 SD6 SD7 SD8 SD9 SD10 ... PO76 PO77 PO78 PO79 PO80 PO81 PO82 PO83 PO84 PO85
count 9822.000000 9822.000000 9822.000000 9822.000000 9822.000000 9822.000000 9822.000000 9822.000000 9822.000000 9822.000000 ... 9822.000000 9822.000000 9822.000000 9822.000000 9822.000000 9822.000000 9822.000000 9822.00000 9822.000000 9822.000000
mean 24.253207 1.108735 2.677561 2.996437 5.779067 0.700672 4.637650 1.050092 3.262981 6.188964 ... 0.079821 0.004582 0.007941 0.004276 0.574018 0.000916 0.005091 0.03146 0.008450 0.013846
std 12.918058 0.412101 0.780701 0.804660 2.874148 1.015107 1.721212 1.011156 1.606287 1.896070 ... 0.384431 0.067535 0.088764 0.071224 0.561255 0.030258 0.077996 0.20907 0.092647 0.117728
min 1.000000 1.000000 1.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.000000
25% 10.000000 1.000000 2.000000 2.000000 3.000000 0.000000 4.000000 0.000000 2.000000 5.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.000000
50% 30.000000 1.000000 3.000000 3.000000 7.000000 0.000000 5.000000 1.000000 3.000000 6.000000 ... 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.00000 0.000000 0.000000
75% 35.000000 1.000000 3.000000 3.000000 8.000000 1.000000 6.000000 2.000000 4.000000 7.000000 ... 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.00000 0.000000 0.000000
max 41.000000 10.000000 6.000000 6.000000 10.000000 9.000000 9.000000 5.000000 9.000000 9.000000 ... 8.000000 1.000000 1.000000 2.000000 7.000000 1.000000 2.000000 4.00000 2.000000 2.000000

8 rows × 85 columns

In [7]:
data.dtypes.value_counts()
Out[7]:
int64     85
object     2
dtype: int64
In [8]:
data.CLASS.value_counts()
Out[8]:
No     9236
Yes     586
Name: CLASS, dtype: int64
In [9]:
sns.countplot(data['CLASS']);
In [10]:
yes = data[data['CLASS']=='Yes']
no = data[data['CLASS']=='No']
In [11]:
yes.SD1.replace((0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41), ("Nan","High Income, expensive child","Very Important Provincials","High status seniors","Affluent senior apartments","Mixed seniors","Career and childcare","Dinki's (double income no kids)","Middle class families","Modern, complete families","Stable family","Family starters","Affluent young families","Young all american family","Junior cosmopolitan","Senior cosmopolitans","Students in apartments","Fresh masters in the city","Single youth","Suburban youth","Etnically diverse","Young urban have-nots","Mixed apartment dwellers","Young and rising","Young, low educated","Young seniors in the city","Own home elderly","Seniors in apartments","Residential elderly","Porchless seniors: no front yard","Religious elderly singles","Low income catholics","Mixed seniors","Lower class large families","Large family, employed child","Village families","Couples with teens 'Married with children","Mixed small town dwellers","Traditional families","Large religous families","Large family farms","Mixed rurals"), inplace=True)
no.SD1.replace((0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41), ("Nan","High Income, expensive child","Very Important Provincials","High status seniors","Affluent senior apartments","Mixed seniors","Career and childcare","Dinki's (double income no kids)","Middle class families","Modern, complete families","Stable family","Family starters","Affluent young families","Young all american family","Junior cosmopolitan","Senior cosmopolitans","Students in apartments","Fresh masters in the city","Single youth","Suburban youth","Etnically diverse","Young urban have-nots","Mixed apartment dwellers","Young and rising","Young, low educated","Young seniors in the city","Own home elderly","Seniors in apartments","Residential elderly","Porchless seniors: no front yard","Religious elderly singles","Low income catholics","Mixed seniors","Lower class large families","Large family, employed child","Village families","Couples with teens 'Married with children","Mixed small town dwellers","Traditional families","Large religous families","Large family farms","Mixed rurals"), inplace=True)
yes.SD5.replace((0,1,2,3,4,5,6,7,8,9,10),("Nan","Successful hedonists","Driven Growers","Average Family","Career Loners","Living well","Cruising Seniors","Retired and Religeous","Family with grown ups","Conservative families","Farmers"), inplace=True)
no.SD5.replace((0,1,2,3,4,5,6,7,8,9,10),("Nan","Successful hedonists","Driven Growers","Average Family","Career Loners","Living well","Cruising Seniors","Retired and Religeous","Family with grown ups","Conservative families","Farmers"), inplace=True)
yes.SD4.replace((1,2,3,4,5,6),("1-20-30 years","2 30-40 years","3-40-50 years","4-50-60 years","5-60-70 years","6-70-80 years"), inplace=True)
no.SD4.replace((1,2,3,4,5,6),("1-20-30 years","2 30-40 years","3-40-50 years","4-50-60 years","5-60-70 years","6-70-80 years"), inplace=True)
no.PO44.replace((0,1,2,3,4,5,6,7,8,9),("f 0","f 1 – 49","f 50 – 99","f 100 – 199","f 200 – 499","f 500 – 999","f 1000 – 4999","f 5000 – 9999","f 10.000 - 19.999"," f 20.000 - ?"), inplace=True)
yes.PO44.replace((0,1,2,3,4,5,6,7,8,9),("f 0","f 1 – 49","f 50 – 99","f 100 – 199","f 200 – 499","f 500 – 999","f 1000 – 4999","f 5000 – 9999","f 10.000 - 19.999"," f 20.000 - ?"), inplace=True)

Exploration des données:

In [12]:
def yes_no(var):
    fig = {
      "data": [
        {
          "values": yes.loc[:,var].value_counts().values,
          "labels": yes.loc[:,var].value_counts().index,
          "domain": {"x": [0, .48]},
          "name": "Yes",
          "hoverinfo":"all",
          "hole": .4,
          "type": "pie"
        },
        {
          "values": no.loc[:,var].value_counts().values,
          "labels": no.loc[:,var].value_counts().index,
          "text":[var],
          "textposition":"inside",
          "domain": {"x": [.52, 1]},
          "name": "No",
          "hoverinfo":"all",
          "hole": .4,
          "type": "pie"
        }],
      "layout": {
            "title": var +" " +"Yes_No Comparaison",
            "annotations": [
                {
                    "font": {
                        "size": 20
                    },
                    "showarrow": True,
                    "text": "Yes",
                    "x": 0.20,
                    "y": 0.5
                },
                {
                    "font": {
                        "size": 20
                    },
                    "showarrow": True,
                    "text": "No",
                    "x": 0.8,
                    "y": 0.5
                }
            ]
        }
    }
    
    pf.iplot(fig, filename='donut')

SD1: Customer Subtype

In [13]:
yes_no('SD1')

On remarque que le plus grand nombre de clients actifs chez AssurExperts Inc, sont ceux qui appartiennent à Lower class large families avec un pourcentage de 13.7% suivi par large family farms 12.3% pour les intéressés

SD4: MGEMLEEF Avg age

In [14]:
yes_no('SD4')

On remarque que le plus grand nombre de clients actifs chez AssurExperts Inc, sont ceux qui appartiennent à la tranche d'age de 40-50 ans avec un pourcentage de 51.7% . Cette tranche d'age représente une majorité suivi par les 30-40 ans avec 26.6% ensuite les 50-60 ans avec un 17.9%.

SD5: Customer main type

In [15]:
yes_no('SD5')

On remarque que le plus grand nombre de clients actifs chez AssurExperts Inc, sont ceux des family with grown ups avec 25%.

SD16: High level education

In [16]:
yes_no('SD16')

SD17: Medium level education

In [17]:
yes_no('SD17')

SD18: Lower level education

In [18]:
yes_no('SD18')
In [19]:
trace1 = go.Bar(
    x=data.CLASS,
    y=data.SD16,
    name='High level education'
)
trace2 = go.Bar(
    x=data.CLASS,
    y=data.SD17,
    name='medium level education'
)
trace3 = go.Bar(
    x=data.CLASS,
    y=data.SD18,
    name='Lower level education'
)

datag2 = [trace1, trace2,trace3]
layout = go.Layout(
    barmode='stack'
)

fig = go.Figure(data=datag2, layout=layout)
iplot(fig, filename='stacked-bar')

On remarque que le plus grand nombre de clients actifs chez AssurExperts Inc, sont ceux qui ont un faible ou moyen niveau d'éducation

SD19,SD20,SD21,SD22,SD23 ET SD24

SD19: High status

In [20]:
yes_no('SD19')

SD20: Entrepreneur

In [21]:
yes_no('SD20')

SD1:Farmer

In [22]:
yes_no('SD21')

SD22: Middle management

In [23]:
yes_no('SD22')

SD23: skilled labourers

In [24]:
yes_no('SD23')
In [25]:
trace1 = go.Bar(
    x=data.CLASS,
    y=data.SD19,
    name='High status'
)
trace2 = go.Bar(
    x=data.CLASS,
    y=data.SD20,
    name='Entrepreneur'
)
trace3 = go.Bar(
    x=data.CLASS,
    y=data.SD21,
    name='Farmer'
)
trace4 = go.Bar(
    x=data.CLASS,
    y=data.SD22,
    name='Middle management'
)
trace5 = go.Bar(
    x=data.CLASS,
    y=data.SD23,
    name='skilled labourers'
)
trace6 = go.Bar(
    x=data.CLASS,
    y=data.SD24,
    name='unskilled labourers'
)
datag2 = [trace1, trace2,trace3,trace4, trace5,trace6]
layout = go.Layout(
    barmode='stack'
)

fig = go.Figure(data=datag2, layout=layout)
iplot(fig, filename='stacked-bar')

On remarque que le plus grand nombre de clients actifs chez AssurExperts Inc, sont des ouvriers non qualifiés,des ouvriers qualifiés,et les cadres intermédiaires.

SD25, SD26, SD27, SD28 et SD29

SD25: SOCIAL CLASS A

In [26]:
yes_no('SD25')

SD26: SOCIAL CLASS B1

In [27]:
yes_no('SD26')

SD27: SOCIAL CLASS B2

In [28]:
yes_no('SD27')

SD28: SOCIAL CLASS C

In [29]:
yes_no('SD28')

D29: SOCIAL CLASS D

In [30]:
yes_no('SD29')
In [31]:
trace1 = go.Bar(
    x=data.CLASS,
    y=data.SD25,
    name='SOCIAL CLASS A'
)
trace2 = go.Bar(
    x=data.CLASS,
    y=data.SD26,
    name='SOCIAL CLASS B1'
)
trace3 = go.Bar(
    x=data.CLASS,
    y=data.SD27,
    name='SOCIAL CLASS B2'
)
trace4 = go.Bar(
    x=data.CLASS,
    y=data.SD28,
    name='SOCIAL CLASS C'
)
trace5 = go.Bar(
    x=data.CLASS,
    y=data.SD29,
    name='SOCIAL CLASS D'
)
datag2 = [trace1, trace2, trace3, trace4, trace5]
layout = go.Layout(
    barmode='stack'
)

fig = go.Figure(data=datag2, layout=layout)
iplot(fig, filename='stacked-bar')

On remarque que le plus grand nombre de clients actifs chez AssurExperts Inc, sont ceux qui appartiennent à une classe sociale C et ceux qui ont une maison et une voiture

SD30 et SD31

SD30:Rented house

In [32]:
yes_no('SD30')

SD31: Home Owners

In [33]:
yes_no("SD31")
In [34]:
trace1 = go.Bar(
    x=data.CLASS,
    y=data.SD30,
    name='Rented house'
)
trace2 = go.Bar(
    x=data.CLASS,
    y=data.SD31,
    name='Home Owners'
)

datag2 = [trace1, trace2]
layout = go.Layout(
    barmode='stack'
)

fig = go.Figure(data=datag2, layout=layout)
iplot(fig, filename='stacked-bar')

On remarque que le plus grand nombre de clients actifs chez AssurExperts Inc, sont ceux sont propriétaires d'une maison .

SD35 et SD36

SD35: National Health Service

In [35]:
yes_no('SD35')

SD36: Private health insurance

In [36]:
yes_no('SD36')
In [37]:
trace1 = go.Bar(
    x=data.CLASS,
    y=data.SD35,
    name='National Health Service'
)
trace2 = go.Bar(
    x=data.CLASS,
    y=data.SD36,
    name='Private health insurance '
)

datag2 = [trace1, trace2]
layout = go.Layout(
    barmode='stack'
)

fig = go.Figure(data=datag2, layout=layout)
iplot(fig, filename='stacked-bar')

On remarque que le plus grand nombre de clients actifs chez AssurExperts Inc, sont ceux qui ont un service de santé national.

SD42 et SD43

SD42:income

In [38]:
yes_no('SD42')

On remarque que le plus grand nombre de clients actifs chez AssurExperts Inc,sont ceux qui ont un income plus élevé donc plus aisé financièrement.

SD43:Purchasing

In [39]:
yes_no('SD43')

On remarque que le plus grand nombre de clients actifs chez AssurExperts Inc, sont ceux qui sont évidament plus aisés financièrement.

PO44:

In [40]:
yes_no('PO44')

On remarque que la contribution majoritaire chez les individus intéressés est de 50 - 99 et elle représente (55.1%). Alors que pour les individus non intéressés la contribution majoritaire est de 0 (61.2%) par la suite ces derniers sont plus assurés quant à la private third party.

PO47: Contribution car policies

In [41]:
yes_no('PO47')

On remarque que les clients intéressés ont des voitures beaucoup mieux assurés que les indiv non intéressés avec 71.7% , alors que 50.1% des clients non intéressées ont une contribution de 0.

PO59: Contribution fire policies

In [42]:
yes_no('PO59')

On remarque que le plus grand nombre de clients actifs chez AssurExperts Inc, sont ceux qui ont contribué à une police d'incendie.

PO61: boat policies

In [43]:
yes_no('PO61')

On remarque que la modalité majoritaire est la contribution de 0, mais les clients intéressés montrent un pourentage de contribution plus important que celui des non intéressés.

PO65: private third party insurance

In [44]:
yes_no('PO65')

On remarque que le plus grand nombre de clients actifs chez AssurExperts Inc, sont plus porbables d'avoir 1 private third party insurance, alors que ceux qui ne le sont pas intéressés sont plus probable d'en avoir 0.

PO68: Number of car policies

In [45]:
yes_no('PO68')

On remarque que le plus grand nombre de clients actifs chez AssurExperts Inc, sont plus porbable d'avoir 1 car policy, alors que ceux qui ne sont pas intéressés sont plus probable d'en avoir 0.

PO82: Number of boat policies

In [46]:
yes_no('PO82')

On remarque, que les deux classes montrent une probablité forte d'avoir 0 boat policies, mais le plus grand nombre des clients actifs chez AssurExperts Inc, sont plus porbable d'avoir 1, boat policies, alors que ceux qui ne sont pas intéressés sont plus probable d'en avoir 0.

PO85: Number of social security insurance policies

In [47]:
yes_no('PO85')

On remarque, que les deux classes montrent une probablité forte d'avoir 0 social security insurance policies , mais le plus grand nombre des clients actifs chez AssurExperts Inc, sont plus porbable d'avoir 1,ecurity insurance policies, alors que ceux qui ne sont pas intéressés sont plus probable d'en avoir 0.

Conclusion: Profil des clients intéressés

D'après les conclusions ci-dessus, nous pouvons remarquer que le profil des clients intéressés de l'assurance caravane exige les criteres suivants:

  • Âge compris entre 30 et 60 ans
  • A une voiture
  • A une maison
  • Driven Growers, famille moyenne et famille avec adultes
  • Classe sociale A

Etude des corrélations

In [48]:
data.CLASS = pd.factorize(data.CLASS)[0]
In [49]:
correlations_class = data.corr(method="pearson").loc['CLASS'].sort_values()
In [50]:
correlations_class.tail(15)
Out[50]:
SD25     0.064810
SD10     0.067168
SD32     0.070665
SD31     0.075283
PO61     0.075779
PO82     0.082763
SD16     0.084373
SD42     0.085122
PO65     0.091379
PO59     0.096709
PO44     0.098757
SD43     0.099018
PO68     0.126768
PO47     0.137053
CLASS    1.000000
Name: CLASS, dtype: float64
In [51]:
correlations_class.head(15)
Out[51]:
SD18   -0.084835
SD37   -0.079035
SD30   -0.075743
SD34   -0.073395
SD1    -0.060742
SD35   -0.060607
SD5    -0.059306
SD12   -0.058787
SD21   -0.057811
SD13   -0.054349
SD24   -0.053767
SD29   -0.050652
SD28   -0.046842
PO54   -0.036581
PO75   -0.036398
Name: CLASS, dtype: float64
In [52]:
correlations = data.corr(method="pearson").abs()
In [53]:
uns = correlations.unstack()
In [54]:
sorted_correlations = uns.sort_values(kind="quicksort",ascending=False)
high_correlations = sorted_correlations[(0.8<sorted_correlations) & (sorted_correlations< 1.0)]
In [55]:
high_correlations = high_correlations.drop_duplicates()
In [56]:
high_correlations
Out[56]:
SD30  SD31    0.999625
SD36  SD35    0.999381
SD1   SD5     0.992712
PO46  PO67    0.984484
PO44  PO65    0.981097
PO57  PO78    0.979788
PO75  PO54    0.967662
PO85  PO64    0.964774
PO51  PO72    0.962867
PO79  PO58    0.959882
PO81  PO60    0.942761
PO62  PO83    0.933068
PO49  PO70    0.923728
PO73  PO52    0.913831
PO74  PO53    0.908873
PO47  PO68    0.907506
PO66  PO45    0.906663
PO50  PO71    0.901122
PO56  PO77    0.898726
PO48  PO69    0.894309
PO61  PO82    0.894000
SD12  SD10    0.883251
PO59  PO80    0.869354
PO63  PO84    0.865845
PO76  PO55    0.852969
dtype: float64

Recursive Feature Elimination


Dans les cellules suivantes, nous allons utiliser RFE avec plusieurs modèles afain d'extraire l'importance de chaque feature.
Après, nous allons choisir les features avec une moyenne de ranking elevée.

In [57]:
Train = data.loc[data['STATUS'] == 'Learning']
Train = Train.drop(columns='STATUS', axis=1)
X_train = Train.drop(columns='CLASS', axis=1)
y_train = Train.CLASS
In [58]:
Test = data.loc[data['STATUS'] == 'Test']
Test = Test.drop(columns='STATUS', axis=1)
X_test = Test.drop(columns='CLASS', axis=1)
y_test = Test.CLASS
In [59]:
X=data.iloc[:,:-2]
Y = data.CLASS
colnames = X.columns
In [60]:
classifier = LinearRegression()
classifier.fit(X, Y)
Out[60]:
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,
         normalize=False)
In [61]:
from sklearn.preprocessing import MinMaxScaler
ranks = {}

def ranking(ranks, names, order=1):
    minmax = MinMaxScaler()
    ranks = minmax.fit_transform(order*np.array([ranks]).T).T[0]
    ranks = map(lambda x: round(x,2), ranks)
    return dict(zip(names, ranks))
In [62]:
rfe = RFE(classifier, n_features_to_select=1)
In [63]:
#Using Linear Regression
lr = LinearRegression(normalize=True)
lr.fit(X,Y)
ranks["LinReg"] = ranking(np.abs(lr.coef_), colnames)

# Using Ridge 
ridge = Ridge(alpha = 7)
ridge.fit(X,Y)
ranks['Ridge'] = ranking(np.abs(ridge.coef_), colnames)

# Using Lasso
lasso = Lasso(alpha=.05)
lasso.fit(X, Y)
ranks["Lasso"] = ranking(np.abs(lasso.coef_), colnames)

# Using Random Forest
rf = RandomForestRegressor(n_jobs=-1, n_estimators=50, verbose=3)
rf.fit(X,Y)
ranks["RF"] = ranking(rf.feature_importances_, colnames);

# Using XGBoost
xg = xgb.XGBClassifier(learning_rate=0.1, n_estimators=10, objective='binary:logistic')
xg.fit(X,Y)
ranks["XG"] = ranking(xg.feature_importances_, colnames);

# Using LightGBM
lgbm = LGBMClassifier(objective='binary',class_weight='balanced')
lgbm.fit(X, Y)
ranks["LGBM"] = ranking(lgbm.feature_importances_, colnames);

# Using SVM
svm = SVC(kernel='linear', probability=True,random_state = 5)
svm.fit(X=X, y=Y)
ranks["SVM"] = ranking(svm.coef_.ravel(), colnames);
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 8 concurrent workers.
building tree 1 of 50building tree 2 of 50building tree 3 of 50building tree 4 of 50building tree 5 of 50

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[Parallel(n_jobs=-1)]: Done  16 tasks      | elapsed:    0.3s
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[Parallel(n_jobs=-1)]: Done  50 out of  50 | elapsed:    1.0s finished
C:\Users\yassi\Anaconda3\lib\site-packages\sklearn\utils\validation.py:590: DataConversionWarning:

Data with input dtype int32 was converted to float64 by MinMaxScaler.

In [64]:
s
r = {}
for name in colnames:
    r[name] = round(np.mean([ranks[method][name] 
                             for method in ranks.keys()]), 2)
 
methods = sorted(ranks.keys())
ranks["Mean"] = r
methods.append("Mean")
 
print("\t%s" % "\t".join(methods))
for name in colnames:
    print("%s\t%s" % (name, "\t".join(map(str, 
                         [ranks[method][name] for method in methods]))))
	LGBM	Lasso	LinReg	RF	Ridge	SVM	XG	Mean
SD1	1.0	0.15	0.0	0.56	0.01	0.85	0.06	0.38
SD2	0.11	0.0	0.01	0.08	0.02	0.86	0.0	0.15
SD3	0.17	0.0	0.0	0.13	0.01	0.85	0.0	0.17
SD4	0.14	0.0	0.02	0.15	0.06	0.85	0.0	0.17
SD5	0.01	0.0	0.01	0.23	0.04	0.86	0.0	0.16
SD6	0.24	0.0	0.0	0.14	0.01	0.85	0.0	0.18
SD7	0.47	0.0	0.0	0.27	0.01	0.86	0.0	0.23
SD8	0.28	0.0	0.01	0.19	0.02	0.85	0.0	0.19
SD9	0.35	0.0	0.0	0.24	0.0	0.86	0.0	0.21
SD10	0.35	0.0	0.02	0.19	0.05	0.85	0.0	0.21
SD11	0.3	0.0	0.0	0.12	0.01	0.85	0.0	0.18
SD12	0.28	0.0	0.01	0.14	0.04	0.85	0.0	0.19
SD13	0.27	0.0	0.02	0.18	0.05	0.85	0.0	0.2
SD14	0.33	0.0	0.02	0.28	0.05	0.85	0.0	0.22
SD15	0.37	0.0	0.01	0.24	0.04	0.85	0.0	0.22
SD16	0.26	0.0	0.01	0.26	0.04	0.86	0.0	0.2
SD17	0.27	0.0	0.0	0.27	0.01	0.86	0.0	0.2
SD18	0.31	0.0	0.01	0.25	0.04	0.85	0.31	0.25
SD19	0.31	0.0	0.0	0.21	0.01	0.86	0.0	0.2
SD20	0.16	0.0	0.01	0.17	0.02	0.86	0.0	0.17
SD21	0.32	0.0	0.03	0.09	0.08	0.85	0.0	0.2
SD22	0.49	0.0	0.0	0.29	0.01	0.86	0.44	0.3
SD23	0.46	0.0	0.0	0.24	0.0	0.86	0.0	0.22
SD24	0.43	0.0	0.0	0.25	0.0	0.86	0.0	0.22
SD25	0.19	0.0	0.01	0.23	0.01	0.86	0.0	0.19
SD26	0.4	0.0	0.01	0.23	0.03	0.86	0.0	0.22
SD27	0.37	0.0	0.01	0.2	0.02	0.86	0.0	0.21
SD28	0.46	0.0	0.0	0.24	0.01	0.86	0.0	0.22
SD29	0.32	0.0	0.0	0.16	0.0	0.86	0.0	0.19
SD30	0.39	0.01	0.06	0.15	0.18	0.84	0.0	0.23
SD31	0.13	0.0	0.06	0.15	0.16	0.84	0.0	0.19
SD32	0.5	0.0	0.03	0.2	0.08	0.85	0.0	0.24
SD33	0.25	0.0	0.02	0.19	0.06	0.85	0.0	0.2
SD34	0.27	0.0	0.02	0.16	0.05	0.85	0.0	0.19
SD35	0.34	0.0	0.16	0.15	0.34	0.73	0.0	0.25
SD36	0.04	0.0	0.16	0.13	0.34	0.74	0.0	0.2
SD37	0.6	0.0	0.01	0.2	0.04	0.85	0.5	0.31
SD38	0.39	0.0	0.01	0.25	0.03	0.86	0.0	0.22
SD39	0.32	0.0	0.01	0.23	0.02	0.86	0.0	0.21
SD40	0.23	0.0	0.0	0.18	0.01	0.86	0.0	0.18
SD41	0.16	0.0	0.03	0.08	0.09	0.85	0.0	0.17
SD42	0.31	0.0	0.02	0.15	0.05	0.85	0.0	0.2
SD43	0.55	0.0	0.01	0.45	0.03	0.85	0.31	0.31
PO44	0.41	0.0	0.09	0.54	0.24	0.85	0.0	0.3
PO45	0.14	0.0	0.03	0.04	0.07	0.85	0.0	0.16
PO46	0.12	0.0	0.04	0.01	0.12	0.88	0.0	0.17
PO47	0.71	1.0	0.02	0.55	0.07	0.86	0.62	0.55
PO48	0.04	0.0	0.0	0.02	0.0	1.0	0.0	0.15
PO49	0.19	0.0	0.02	0.09	0.05	0.85	0.0	0.17
PO50	0.0	0.0	0.05	0.0	0.09	0.84	0.0	0.14
PO51	0.03	0.0	0.09	0.04	0.16	0.86	0.0	0.17
PO52	0.14	0.0	0.02	0.04	0.04	0.85	0.0	0.16
PO53	0.0	0.0	0.04	0.0	0.09	0.84	0.0	0.14
PO54	0.27	0.0	0.01	0.16	0.03	0.86	0.0	0.19
PO55	0.22	0.0	0.03	0.23	0.08	0.85	0.0	0.2
PO56	0.07	0.0	0.04	0.01	0.09	0.78	0.0	0.14
PO57	0.07	0.0	0.49	0.09	0.5	0.91	0.0	0.29
PO58	0.05	0.0	0.08	0.03	0.11	0.85	0.12	0.18
PO59	0.91	0.0	0.04	1.0	0.11	0.85	1.0	0.56
PO60	0.0	0.0	0.13	0.01	0.45	0.8	0.06	0.21
PO61	0.17	0.0	0.0	0.11	0.18	0.84	0.75	0.29
PO62	0.06	0.0	0.05	0.08	0.06	0.86	0.0	0.16
PO63	0.03	0.0	0.1	0.04	0.22	0.84	0.0	0.18
PO64	0.1	0.0	0.09	0.11	0.19	0.85	0.0	0.19
PO65	0.0	0.0	0.13	0.37	0.33	0.86	0.0	0.24
PO66	0.01	0.0	0.05	0.03	0.11	0.87	0.0	0.15
PO67	0.01	0.0	0.02	0.01	0.04	0.74	0.0	0.12
PO68	0.12	0.0	0.0	0.39	0.0	0.85	0.0	0.19
PO69	0.0	0.0	0.08	0.01	0.21	0.0	0.0	0.04
PO70	0.0	0.0	0.04	0.09	0.11	0.86	0.0	0.16
PO71	0.0	0.0	0.12	0.0	0.16	0.85	0.0	0.16
PO72	0.0	0.0	0.09	0.03	0.11	0.82	0.0	0.15
PO73	0.0	0.0	0.06	0.02	0.15	0.85	0.0	0.15
PO74	0.0	0.0	0.06	0.0	0.09	0.85	0.0	0.14
PO75	0.04	0.0	0.03	0.11	0.06	0.85	0.0	0.16
PO76	0.08	0.0	0.09	0.28	0.24	0.85	0.12	0.24
PO77	0.0	0.0	0.05	0.01	0.07	0.95	0.0	0.15
PO78	0.0	0.0	1.0	0.04	0.69	0.71	0.0	0.35
PO79	0.0	0.0	0.31	0.01	0.3	0.87	0.0	0.21
PO80	0.08	0.0	0.07	0.29	0.18	0.86	0.0	0.21
PO81	0.0	0.0	0.84	0.01	0.46	0.97	0.0	0.33
PO82	0.0	0.0	0.59	0.1	1.0	0.89	0.0	0.37
PO83	0.03	0.0	0.09	0.12	0.19	0.86	0.0	0.18
PO84	0.0	0.0	0.17	0.02	0.35	0.88	0.0	0.2
PO85	0.0	0.0	0.17	0.06	0.24	0.85	0.0	0.19
In [65]:
meanplot = pd.DataFrame(list(r.items()), columns= ['Feature','Mean Ranking'])


meanplot = meanplot.sort_values('Mean Ranking', ascending=False)
In [66]:
sns.factorplot(x="Mean Ranking", y="Feature", data = meanplot, kind="bar", 
               size=14, aspect=1.9, palette='coolwarm')
C:\Users\yassi\Anaconda3\lib\site-packages\seaborn\categorical.py:3666: UserWarning:

The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.

C:\Users\yassi\Anaconda3\lib\site-packages\seaborn\categorical.py:3672: UserWarning:

The `size` paramter has been renamed to `height`; please update your code.

Out[66]:
<seaborn.axisgrid.FacetGrid at 0x184a8eb2208>

Features Selected :

Le ranking graph ci-dessus nous permet de dégager les 31 premières features selon leurs moyennes de ranking.
On peut constater aussi que les features PO59, PO47, SD1 et PO82 ont une importance très elevée par rapport aux autres features.
Peut-on encore vérifier l'importance de ces features ?

Feature Selection Using Clustering

Nous allons utiliser K-means afin de diviser ceux qui ont répondu par "Yes" en deux clusters.
Cette technique va nous permettre de voir la répartition de ceux qui ont dit "Yes" sur deux clusters pour mieux comprendre laquelle entre eux est la plus décisive.

In [67]:
df_yes = data.loc[data.CLASS == 1]
df_yes = df_yes.drop('STATUS',1)
In [68]:
kmeans = KMeans(n_clusters=2).fit(df_yes)
In [69]:
df_yes['Cluster'] = kmeans.labels_
In [70]:
df_yes.head()
Out[70]:
SD1 SD2 SD3 SD4 SD5 SD6 SD7 SD8 SD9 SD10 ... PO78 PO79 PO80 PO81 PO82 PO83 PO84 PO85 CLASS Cluster
41 11 1 3 3 3 2 7 0 0 9 ... 0 0 2 0 0 1 0 0 1 1
45 38 1 3 3 9 0 5 1 3 7 ... 0 0 1 0 0 0 0 0 1 0
57 12 1 3 2 3 0 6 0 3 7 ... 0 1 1 0 0 0 0 0 1 1
97 9 1 2 3 3 0 6 1 2 7 ... 0 0 0 0 0 0 0 0 1 1
98 36 1 2 4 8 2 4 2 2 7 ... 0 0 0 0 0 0 0 0 1 0

5 rows × 87 columns

In [127]:
plt.scatter(df_yes.PO47, df_yes.Cluster)
Out[127]:
<matplotlib.collections.PathCollection at 0x184aca63ba8>
In [128]:
plt.scatter(df_yes.PO59, df_yes.Cluster)
Out[128]:
<matplotlib.collections.PathCollection at 0x184ac98e978>
In [132]:
plt.scatter(df_yes.PO82, df_yes.Cluster)
Out[132]:
<matplotlib.collections.PathCollection at 0x184acad7588>
In [133]:
plt.scatter(df_yes.SD1, df_yes.Cluster)
Out[133]:
<matplotlib.collections.PathCollection at 0x184acb34588>
Interprétation

La feature SD1 est l'une des variables les plus importantes dans le choix d'une assurance caravane. Elle peut même nous donner une idée sur les profils susceptibles à chosir ce type d'assurances.

In [72]:
selected_features = meanplot.head(31).Feature
In [73]:
df_reduced = data[selected_features]
In [74]:
df_reduced['CLASS'] = data.CLASS
In [75]:
df_reduced.to_csv('df_reduced.csv', sep='\t')
In [83]:
df_reduced.head()
Out[83]:
PO59 PO47 SD1 PO82 PO78 PO81 SD37 SD43 PO44 SD22 ... SD26 SD15 SD14 SD23 PO80 PO79 SD39 SD10 PO60 CLASS
0 5 6 33 0 0 0 0 3 0 2 ... 1 6 2 5 1 0 5 7 0 0
1 2 0 37 0 0 0 2 4 2 5 ... 2 5 4 0 1 0 5 6 0 0
2 2 6 37 0 0 0 4 4 2 7 ... 5 2 4 0 1 0 0 3 0 0
3 2 6 9 0 0 0 1 4 0 3 ... 2 4 3 1 1 0 3 5 0 0
4 6 0 40 0 0 0 0 3 0 0 ... 0 4 4 0 1 0 9 7 0 0

5 rows × 32 columns

Associativité:

In [100]:
ass_yes = pd.read_csv('df_reduced.csv', header=0, sep='\t')
ass_yes=ass_yes.loc[ass_yes['CLASS'] == 1]

ass_yes = ass_yes.drop(columns='CLASS', axis=1)

ass_yes = ass_yes.drop(columns='Unnamed: 0', axis=1)
In [101]:
ass_yes.head()
Out[101]:
PO59 PO47 SD1 PO82 PO78 PO81 SD37 SD43 PO44 SD22 ... SD28 SD26 SD15 SD14 SD23 PO80 PO79 SD39 SD10 PO60
41 4 6 11 0 0 0 2 6 2 4 ... 2 2 4 3 0 2 0 3 9 0
45 3 6 38 0 0 0 4 4 2 2 ... 5 1 5 2 3 1 0 1 7 0
57 4 0 12 0 0 0 2 7 2 5 ... 4 3 6 2 2 1 1 4 7 0
97 0 0 9 0 0 0 0 4 0 5 ... 0 8 4 1 1 0 0 4 7 0
98 0 0 36 0 0 0 2 3 0 3 ... 4 1 3 4 1 0 0 3 7 0

5 rows × 31 columns

Dummy variable:

In [102]:
def process_var(var):
    global ass_yes
    # encoding into 3 categories:
    var_dummies = pd.get_dummies(ass_yes[var], prefix=var)
    
    # adding dummy variables
    ass_yes = pd.concat([ass_yes,var_dummies],axis=1)
    # removing "Pclass"
    ass_yes.drop(var,axis=1,inplace=True)
In [103]:
for v in ass_yes.columns:
    process_var(v)
In [104]:
ass_yes.head()
Out[104]:
PO59_0 PO59_1 PO59_2 PO59_3 PO59_4 PO59_5 PO59_6 PO47_0 PO47_5 PO47_6 ... SD10_3 SD10_4 SD10_5 SD10_6 SD10_7 SD10_8 SD10_9 PO60_0 PO60_1 PO60_2
41 0 0 0 0 1 0 0 0 0 1 ... 0 0 0 0 0 0 1 1 0 0
45 0 0 0 1 0 0 0 0 0 1 ... 0 0 0 0 1 0 0 1 0 0
57 0 0 0 0 1 0 0 1 0 0 ... 0 0 0 0 1 0 0 1 0 0
97 1 0 0 0 0 0 0 1 0 0 ... 0 0 0 0 1 0 0 1 0 0
98 1 0 0 0 0 0 0 1 0 0 ... 0 0 0 0 1 0 0 1 0 0

5 rows × 239 columns

Apriori:

In [136]:
#Apriori
freq_items = apriori(ass_yes, min_support = 0.64, use_colnames=True)
rules = association_rules(freq_items, metric="confidence", min_threshold=0.6)
#sort the rules in non-decreasing lift value
rules = rules.sort_values(ascending=False, by="confidence")
In [138]:
rules
Out[138]:
antecedents consequents antecedent support consequent support support confidence lift leverage conviction
6689 (PO79_0, PO61_0, PO47_6, PO60_0, PO57_0) (PO78_0) 0.674061 0.977816 0.674061 1.000000 1.022688 0.014954 inf
782 (PO82_0, PO47_6, PO60_0) (PO81_0) 0.694539 0.994881 0.694539 1.000000 1.005146 0.003556 inf
9816 (PO78_0, PO79_0, PO60_0, PO81_0, PO82_0) (PO61_0, PO57_0) 0.940273 0.950512 0.940273 1.000000 1.052065 0.046532 inf
9817 (PO78_0, PO79_0, PO60_0, PO57_0, PO82_0) (PO61_0, PO81_0) 0.940273 0.969283 0.940273 1.000000 1.031690 0.028882 inf
9818 (PO78_0, PO79_0, PO81_0, PO57_0, PO82_0) (PO61_0, PO60_0) 0.940273 0.969283 0.940273 1.000000 1.031690 0.028882 inf
3076 (PO78_0, PO79_0, PO81_0, PO47_6) (PO57_0) 0.694539 0.977816 0.694539 1.000000 1.022688 0.015408 inf
7995 (PO61_0, PO79_0, PO81_0, PO76_0) (PO82_0, PO60_0) 0.877133 0.969283 0.877133 1.000000 1.031690 0.026943 inf
783 (PO81_0, PO47_6, PO82_0) (PO60_0) 0.694539 0.994881 0.694539 1.000000 1.005146 0.003556 inf
7993 (PO61_0, PO79_0, PO76_0, PO60_0) (PO81_0, PO82_0) 0.877133 0.969283 0.877133 1.000000 1.031690 0.026943 inf
1732 (PO61_0, PO79_0, PO60_0) (PO82_0) 0.962457 0.972696 0.962457 1.000000 1.028070 0.026279 inf
7990 (PO79_0, PO60_0, PO81_0, PO76_0, PO82_0) (PO61_0) 0.877133 0.972696 0.877133 1.000000 1.028070 0.023949 inf
7989 (PO79_0, PO61_0, PO81_0, PO76_0, PO82_0) (PO60_0) 0.877133 0.994881 0.877133 1.000000 1.005146 0.004490 inf
9824 (PO79_0, PO61_0, PO60_0, PO81_0, PO57_0) (PO78_0, PO82_0) 0.940273 0.950512 0.940273 1.000000 1.052065 0.046532 inf
7988 (PO79_0, PO61_0, PO60_0, PO76_0, PO82_0) (PO81_0) 0.877133 0.994881 0.877133 1.000000 1.005146 0.004490 inf
9826 (PO79_0, PO61_0, PO60_0, PO57_0, PO82_0) (PO78_0, PO81_0) 0.940273 0.972696 0.940273 1.000000 1.028070 0.025673 inf
7986 (PO79_0, PO61_0, PO60_0, PO81_0, PO76_0) (PO82_0) 0.877133 0.972696 0.877133 1.000000 1.028070 0.023949 inf
1735 (PO79_0, PO82_0, PO60_0) (PO61_0) 0.962457 0.972696 0.962457 1.000000 1.028070 0.026279 inf
9813 (PO78_0, PO79_0, PO61_0, PO81_0, PO82_0) (PO57_0, PO60_0) 0.940273 0.972696 0.940273 1.000000 1.028070 0.025673 inf
1816 (PO79_0, PO78_0, PO81_0) (PO57_0) 0.965870 0.977816 0.965870 1.000000 1.022688 0.021427 inf
8000 (PO79_0, PO82_0, PO76_0, PO60_0) (PO61_0, PO81_0) 0.877133 0.969283 0.877133 1.000000 1.031690 0.026943 inf
8001 (PO79_0, PO81_0, PO76_0, PO82_0) (PO61_0, PO60_0) 0.877133 0.969283 0.877133 1.000000 1.031690 0.026943 inf
9802 (PO78_0, PO79_0, PO61_0, PO60_0, PO81_0, PO57_0) (PO82_0) 0.940273 0.972696 0.940273 1.000000 1.028070 0.025673 inf
9803 (PO78_0, PO79_0, PO61_0, PO60_0, PO81_0, PO82_0) (PO57_0) 0.940273 0.977816 0.940273 1.000000 1.022688 0.020859 inf
9804 (PO78_0, PO79_0, PO61_0, PO60_0, PO57_0, PO82_0) (PO81_0) 0.940273 0.994881 0.940273 1.000000 1.005146 0.004814 inf
9805 (PO78_0, PO79_0, PO61_0, PO81_0, PO57_0, PO82_0) (PO60_0) 0.940273 0.994881 0.940273 1.000000 1.005146 0.004814 inf
9806 (PO78_0, PO79_0, PO60_0, PO81_0, PO57_0, PO82_0) (PO61_0) 0.940273 0.972696 0.940273 1.000000 1.028070 0.025673 inf
4546 (PO61_0, PO76_0, PO57_0, PO60_0) (PO82_0) 0.863481 0.972696 0.863481 1.000000 1.028070 0.023576 inf
9812 (PO78_0, PO79_0, PO61_0, PO81_0, PO57_0) (PO82_0, PO60_0) 0.940273 0.969283 0.940273 1.000000 1.031690 0.028882 inf
9808 (PO79_0, PO61_0, PO60_0, PO81_0, PO57_0, PO82_0) (PO78_0) 0.940273 0.977816 0.940273 1.000000 1.022688 0.020859 inf
9809 (PO78_0, PO79_0, PO61_0, PO60_0, PO81_0) (PO57_0, PO82_0) 0.940273 0.950512 0.940273 1.000000 1.052065 0.046532 inf
9810 (PO78_0, PO79_0, PO61_0, PO60_0, PO57_0) (PO81_0, PO82_0) 0.940273 0.969283 0.940273 1.000000 1.031690 0.028882 inf
797 (PO57_0, PO47_6, PO82_0) (PO61_0) 0.677474 0.972696 0.677474 1.000000 1.028070 0.018498 inf
9811 (PO78_0, PO79_0, PO61_0, PO60_0, PO82_0) (PO81_0, PO57_0) 0.940273 0.972696 0.940273 1.000000 1.028070 0.025673 inf
794 (PO61_0, PO57_0, PO47_6) (PO82_0) 0.677474 0.972696 0.677474 1.000000 1.028070 0.018498 inf
9827 (PO79_0, PO61_0, PO81_0, PO57_0, PO82_0) (PO78_0, PO60_0) 0.940273 0.972696 0.940273 1.000000 1.028070 0.025673 inf
9828 (PO79_0, PO60_0, PO81_0, PO57_0, PO82_0) (PO61_0, PO78_0) 0.940273 0.950512 0.940273 1.000000 1.052065 0.046532 inf
5358 (PO79_0, PO76_0, PO57_0, PO60_0) (PO81_0) 0.882253 0.994881 0.882253 1.000000 1.005146 0.004517 inf
3526 (PO61_0, PO78_0, PO81_0, PO57_0) (PO82_0) 0.947099 0.972696 0.947099 1.000000 1.028070 0.025859 inf
5418 (PO61_0, PO79_0, PO81_0, PO76_0) (PO60_0) 0.877133 0.994881 0.877133 1.000000 1.005146 0.004490 inf
1788 (PO78_0, PO81_0, PO76_0) (PO57_0) 0.889078 0.977816 0.889078 1.000000 1.022688 0.019724 inf
9851 (PO61_0, PO79_0, PO57_0, PO60_0) (PO78_0, PO81_0, PO82_0) 0.940273 0.947099 0.940273 1.000000 1.055856 0.049741 inf
4495 (PO79_0, PO80_1, PO82_0) (PO81_0, PO60_0) 0.648464 0.994881 0.648464 1.000000 1.005146 0.003320 inf
9853 (PO61_0, PO79_0, PO81_0, PO57_0) (PO78_0, PO82_0, PO60_0) 0.940273 0.947099 0.940273 1.000000 1.055856 0.049741 inf
1790 (PO57_0, PO81_0, PO76_0) (PO78_0) 0.889078 0.977816 0.889078 1.000000 1.022688 0.019724 inf
3046 (PO61_0, PO78_0, PO81_0, PO47_6) (PO57_0) 0.675768 0.977816 0.675768 1.000000 1.022688 0.014991 inf
9830 (PO61_0, PO78_0, PO79_0, PO60_0) (PO81_0, PO57_0, PO82_0) 0.940273 0.947099 0.940273 1.000000 1.055856 0.049741 inf
3527 (PO61_0, PO78_0, PO81_0, PO82_0) (PO57_0) 0.947099 0.977816 0.947099 1.000000 1.022688 0.021011 inf
1802 (PO78_0, PO80_1, PO81_0) (PO57_0) 0.648464 0.977816 0.648464 1.000000 1.022688 0.014386 inf
9858 (PO79_0, PO82_0, PO57_0, PO60_0) (PO61_0, PO78_0, PO81_0) 0.940273 0.947099 0.940273 1.000000 1.055856 0.049741 inf
9859 (PO79_0, PO81_0, PO57_0, PO82_0) (PO61_0, PO78_0, PO60_0) 0.940273 0.947099 0.940273 1.000000 1.055856 0.049741 inf
1805 (PO80_1, PO81_0, PO57_0) (PO78_0) 0.648464 0.977816 0.648464 1.000000 1.022688 0.014386 inf
5446 (PO61_0, PO79_0, PO80_1, PO60_0) (PO81_0) 0.648464 0.994881 0.648464 1.000000 1.005146 0.003320 inf
5417 (PO61_0, PO79_0, PO76_0, PO60_0) (PO81_0) 0.877133 0.994881 0.877133 1.000000 1.005146 0.004490 inf
3050 (PO61_0, PO57_0, PO81_0, PO47_6) (PO78_0) 0.675768 0.977816 0.675768 1.000000 1.022688 0.014991 inf
1776 (PO61_0, PO81_0, PO57_0) (PO78_0) 0.947099 0.977816 0.947099 1.000000 1.022688 0.021011 inf
1774 (PO61_0, PO78_0, PO81_0) (PO57_0) 0.947099 0.977816 0.947099 1.000000 1.022688 0.021011 inf
752 (PO61_0, PO81_0, PO47_6) (PO82_0) 0.694539 0.972696 0.694539 1.000000 1.028070 0.018964 inf
755 (PO81_0, PO47_6, PO82_0) (PO61_0) 0.694539 0.972696 0.694539 1.000000 1.028070 0.018964 inf
7927 (PO79_0, PO60_0, PO76_0, PO57_0, PO82_0) (PO81_0) 0.856655 0.994881 0.856655 1.000000 1.005146 0.004386 inf
7928 (PO79_0, PO81_0, PO76_0, PO57_0, PO82_0) (PO60_0) 0.856655 0.994881 0.856655 1.000000 1.005146 0.004386 inf
5389 (PO79_0, PO80_1, PO81_0, PO57_0) (PO60_0) 0.643345 0.994881 0.643345 1.000000 1.005146 0.003294 inf
9838 (PO78_0, PO79_0, PO81_0, PO82_0) (PO61_0, PO57_0, PO60_0) 0.940273 0.947099 0.940273 1.000000 1.055856 0.049741 inf
5387 (PO79_0, PO80_1, PO57_0, PO60_0) (PO81_0) 0.643345 0.994881 0.643345 1.000000 1.005146 0.003294 inf
9836 (PO78_0, PO82_0, PO79_0, PO60_0) (PO61_0, PO81_0, PO57_0) 0.940273 0.947099 0.940273 1.000000 1.055856 0.049741 inf
4516 (PO61_0, PO79_0, PO76_0, PO57_0) (PO82_0) 0.860068 0.972696 0.860068 1.000000 1.028070 0.023483 inf
4519 (PO79_0, PO76_0, PO57_0, PO82_0) (PO61_0) 0.860068 0.972696 0.860068 1.000000 1.028070 0.023483 inf
1761 (PO79_0, PO80_1, PO82_0) (PO60_0) 0.648464 0.994881 0.648464 1.000000 1.005146 0.003320 inf
5359 (PO79_0, PO76_0, PO81_0, PO57_0) (PO60_0) 0.882253 0.994881 0.882253 1.000000 1.005146 0.004517 inf
9831 (PO61_0, PO78_0, PO79_0, PO81_0) (PO82_0, PO57_0, PO60_0) 0.940273 0.947099 0.940273 1.000000 1.055856 0.049741 inf
1726 (PO80_1, PO82_0) (PO61_0, PO60_0) 0.653584 0.969283 0.653584 1.000000 1.031690 0.020076 inf
4550 (PO82_0, PO76_0, PO57_0, PO60_0) (PO61_0) 0.863481 0.972696 0.863481 1.000000 1.028070 0.023576 inf
3080 (PO79_0, PO57_0, PO81_0, PO47_6) (PO78_0) 0.694539 0.977816 0.694539 1.000000 1.022688 0.015408 inf
9680 (PO78_0, PO61_0, PO81_0, PO57_0, PO76_0, PO82_0) (PO60_0) 0.863481 0.994881 0.863481 1.000000 1.005146 0.004421 inf
1679 (PO79_0, PO76_0, PO82_0) (PO61_0) 0.880546 0.972696 0.880546 1.000000 1.028070 0.024042 inf
8063 (PO79_0, PO80_1, PO81_0, PO82_0) (PO61_0, PO60_0) 0.648464 0.969283 0.648464 1.000000 1.031690 0.019919 inf
8061 (PO79_0, PO82_0, PO80_1, PO60_0) (PO61_0, PO81_0) 0.648464 0.969283 0.648464 1.000000 1.031690 0.019919 inf
9676 (PO78_0, PO61_0, PO60_0, PO81_0, PO76_0, PO57_0) (PO82_0) 0.863481 0.972696 0.863481 1.000000 1.028070 0.023576 inf
9677 (PO78_0, PO61_0, PO60_0, PO81_0, PO76_0, PO82_0) (PO57_0) 0.863481 0.977816 0.863481 1.000000 1.022688 0.019156 inf
9679 (PO78_0, PO61_0, PO60_0, PO57_0, PO76_0, PO82_0) (PO81_0) 0.863481 0.994881 0.863481 1.000000 1.005146 0.004421 inf
9681 (PO78_0, PO60_0, PO81_0, PO57_0, PO76_0, PO82_0) (PO61_0) 0.863481 0.972696 0.863481 1.000000 1.028070 0.023576 inf
9694 (PO78_0, PO60_0, PO81_0, PO76_0, PO82_0) (PO61_0, PO57_0) 0.863481 0.950512 0.863481 1.000000 1.052065 0.042732 inf
9682 (PO61_0, PO60_0, PO81_0, PO57_0, PO76_0, PO82_0) (PO78_0) 0.863481 0.977816 0.863481 1.000000 1.022688 0.019156 inf
9683 (PO78_0, PO61_0, PO60_0, PO81_0, PO76_0) (PO57_0, PO82_0) 0.863481 0.950512 0.863481 1.000000 1.052065 0.042732 inf
9686 (PO78_0, PO61_0, PO60_0, PO57_0, PO76_0) (PO81_0, PO82_0) 0.863481 0.969283 0.863481 1.000000 1.031690 0.026523 inf
9687 (PO78_0, PO61_0, PO60_0, PO76_0, PO82_0) (PO81_0, PO57_0) 0.863481 0.972696 0.863481 1.000000 1.028070 0.023576 inf
9689 (PO78_0, PO61_0, PO81_0, PO57_0, PO76_0) (PO82_0, PO60_0) 0.863481 0.969283 0.863481 1.000000 1.031690 0.026523 inf
9690 (PO78_0, PO61_0, PO81_0, PO76_0, PO82_0) (PO57_0, PO60_0) 0.863481 0.972696 0.863481 1.000000 1.028070 0.023576 inf
1676 (PO61_0, PO79_0, PO76_0) (PO82_0) 0.880546 0.972696 0.880546 1.000000 1.028070 0.024042 inf
8070 (PO61_0, PO79_0, PO80_1) (PO82_0, PO81_0, PO60_0) 0.648464 0.969283 0.648464 1.000000 1.031690 0.019919 inf
5328 (PO61_0, PO79_0, PO81_0, PO57_0) (PO60_0) 0.940273 0.994881 0.940273 1.000000 1.005146 0.004814 inf
8077 (PO79_0, PO80_1, PO82_0) (PO61_0, PO81_0, PO60_0) 0.648464 0.969283 0.648464 1.000000 1.031690 0.019919 inf
880 (PO78_0, PO81_0, PO47_6) (PO57_0) 0.696246 0.977816 0.696246 1.000000 1.022688 0.015446 inf
881 (PO47_6, PO81_0, PO57_0) (PO78_0) 0.696246 0.977816 0.696246 1.000000 1.022688 0.015446 inf
5327 (PO61_0, PO79_0, PO57_0, PO60_0) (PO81_0) 0.940273 0.994881 0.940273 1.000000 1.005146 0.004814 inf
5299 (PO61_0, PO76_0, PO81_0, PO57_0) (PO60_0) 0.863481 0.994881 0.863481 1.000000 1.005146 0.004421 inf
3106 (PO78_0, PO81_0, PO47_6, PO60_0) (PO57_0) 0.696246 0.977816 0.696246 1.000000 1.022688 0.015446 inf
3107 (PO78_0, PO57_0, PO47_6, PO60_0) (PO81_0) 0.696246 0.994881 0.696246 1.000000 1.005146 0.003564 inf
3108 (PO78_0, PO57_0, PO81_0, PO47_6) (PO60_0) 0.696246 0.994881 0.696246 1.000000 1.005146 0.003564 inf
3468 (PO79_0, PO76_0, PO81_0, PO47_6) (PO60_0) 0.646758 0.994881 0.646758 1.000000 1.005146 0.003311 inf
3467 (PO79_0, PO76_0, PO47_6, PO60_0) (PO81_0) 0.646758 0.994881 0.646758 1.000000 1.005146 0.003311 inf
5298 (PO61_0, PO76_0, PO57_0, PO60_0) (PO81_0) 0.863481 0.994881 0.863481 1.000000 1.005146 0.004421 inf
8110 (PO79_0, PO61_0, PO60_0, PO76_0, PO57_0) (PO82_0) 0.856655 0.972696 0.856655 1.000000 1.028070 0.023390 inf
3110 (PO57_0, PO81_0, PO47_6, PO60_0) (PO78_0) 0.696246 0.977816 0.696246 1.000000 1.022688 0.015446 inf
3111 (PO78_0, PO47_6, PO60_0) (PO81_0, PO57_0) 0.696246 0.972696 0.696246 1.000000 1.028070 0.019010 inf
8058 (PO61_0, PO79_0, PO80_1, PO82_0) (PO81_0, PO60_0) 0.648464 0.994881 0.648464 1.000000 1.005146 0.003320 inf
9696 (PO78_0, PO60_0, PO57_0, PO76_0, PO82_0) (PO61_0, PO81_0) 0.863481 0.969283 0.863481 1.000000 1.031690 0.026523 inf
1722 (PO61_0, PO80_1) (PO82_0, PO60_0) 0.653584 0.969283 0.653584 1.000000 1.031690 0.020076 inf
4579 (PO79_0, PO82_0, PO57_0, PO60_0) (PO61_0) 0.940273 0.972696 0.940273 1.000000 1.028070 0.025673 inf
9727 (PO61_0, PO57_0, PO76_0, PO60_0) (PO78_0, PO81_0, PO82_0) 0.863481 0.947099 0.863481 1.000000 1.055856 0.045679 inf
8051 (PO79_0, PO61_0, PO80_1, PO81_0, PO82_0) (PO60_0) 0.648464 0.994881 0.648464 1.000000 1.005146 0.003320 inf
9730 (PO61_0, PO57_0, PO81_0, PO76_0) (PO78_0, PO82_0, PO60_0) 0.863481 0.947099 0.863481 1.000000 1.055856 0.045679 inf
8049 (PO79_0, PO61_0, PO60_0, PO80_1, PO82_0) (PO81_0) 0.648464 0.994881 0.648464 1.000000 1.005146 0.003320 inf
8048 (PO79_0, PO61_0, PO60_0, PO80_1, PO81_0) (PO82_0) 0.648464 0.972696 0.648464 1.000000 1.028070 0.017706 inf
9737 (PO57_0, PO82_0, PO76_0, PO60_0) (PO61_0, PO78_0, PO81_0) 0.863481 0.947099 0.863481 1.000000 1.055856 0.045679 inf
9738 (PO57_0, PO81_0, PO76_0, PO82_0) (PO61_0, PO78_0, PO60_0) 0.863481 0.947099 0.863481 1.000000 1.055856 0.045679 inf
853 (PO82_0, PO47_6, PO60_0) (PO61_0) 0.694539 0.972696 0.694539 1.000000 1.028070 0.018964 inf
1704 (PO61_0, PO79_0, PO80_1) (PO82_0) 0.648464 0.972696 0.648464 1.000000 1.028070 0.017706 inf
1707 (PO79_0, PO80_1, PO82_0) (PO61_0) 0.648464 0.972696 0.648464 1.000000 1.028070 0.017706 inf
4576 (PO61_0, PO79_0, PO57_0, PO60_0) (PO82_0) 0.940273 0.972696 0.940273 1.000000 1.028070 0.025673 inf
1718 (PO61_0, PO80_1, PO60_0) (PO82_0) 0.653584 0.972696 0.653584 1.000000 1.028070 0.017845 inf
1719 (PO61_0, PO80_1, PO82_0) (PO60_0) 0.653584 0.994881 0.653584 1.000000 1.005146 0.003346 inf
1721 (PO82_0, PO80_1, PO60_0) (PO61_0) 0.653584 0.972696 0.653584 1.000000 1.028070 0.017845 inf
1693 (PO60_0, PO76_0, PO82_0) (PO61_0) 0.883959 0.972696 0.883959 1.000000 1.028070 0.024135 inf
1692 (PO61_0, PO76_0, PO60_0) (PO82_0) 0.883959 0.972696 0.883959 1.000000 1.028070 0.024135 inf
9721 (PO78_0, PO81_0, PO76_0, PO82_0) (PO61_0, PO57_0, PO60_0) 0.863481 0.947099 0.863481 1.000000 1.055856 0.045679 inf
9718 (PO78_0, PO82_0, PO76_0, PO60_0) (PO61_0, PO81_0, PO57_0) 0.863481 0.947099 0.863481 1.000000 1.055856 0.045679 inf
836 (PO61_0, PO79_0, PO47_6) (PO82_0) 0.694539 0.972696 0.694539 1.000000 1.028070 0.018964 inf
8052 (PO79_0, PO60_0, PO80_1, PO81_0, PO82_0) (PO61_0) 0.648464 0.972696 0.648464 1.000000 1.028070 0.017706 inf
839 (PO79_0, PO47_6, PO82_0) (PO61_0) 0.694539 0.972696 0.694539 1.000000 1.028070 0.018964 inf
8054 (PO61_0, PO79_0, PO80_1, PO60_0) (PO81_0, PO82_0) 0.648464 0.969283 0.648464 1.000000 1.031690 0.019919 inf
9708 (PO61_0, PO78_0, PO81_0, PO76_0) (PO82_0, PO57_0, PO60_0) 0.863481 0.947099 0.863481 1.000000 1.055856 0.045679 inf
9705 (PO61_0, PO78_0, PO76_0, PO60_0) (PO81_0, PO57_0, PO82_0) 0.863481 0.947099 0.863481 1.000000 1.055856 0.045679 inf
9703 (PO60_0, PO81_0, PO57_0, PO76_0, PO82_0) (PO61_0, PO78_0) 0.863481 0.950512 0.863481 1.000000 1.052065 0.042732 inf
9702 (PO61_0, PO81_0, PO57_0, PO76_0, PO82_0) (PO78_0, PO60_0) 0.863481 0.972696 0.863481 1.000000 1.028070 0.023576 inf
9701 (PO61_0, PO60_0, PO57_0, PO76_0, PO82_0) (PO78_0, PO81_0) 0.863481 0.972696 0.863481 1.000000 1.028070 0.023576 inf
8057 (PO61_0, PO79_0, PO80_1, PO81_0) (PO82_0, PO60_0) 0.648464 0.969283 0.648464 1.000000 1.031690 0.019919 inf
9698 (PO61_0, PO60_0, PO81_0, PO57_0, PO76_0) (PO78_0, PO82_0) 0.863481 0.950512 0.863481 1.000000 1.052065 0.042732 inf
850 (PO61_0, PO47_6, PO60_0) (PO82_0) 0.694539 0.972696 0.694539 1.000000 1.028070 0.018964 inf
9697 (PO78_0, PO81_0, PO57_0, PO76_0, PO82_0) (PO61_0, PO60_0) 0.863481 0.969283 0.863481 1.000000 1.031690 0.026523 inf
5448 (PO61_0, PO79_0, PO80_1, PO81_0) (PO60_0) 0.648464 0.994881 0.648464 1.000000 1.005146 0.003320 inf
1818 (PO79_0, PO81_0, PO57_0) (PO78_0) 0.965870 0.977816 0.965870 1.000000 1.022688 0.021427 inf
8114 (PO79_0, PO60_0, PO76_0, PO57_0, PO82_0) (PO61_0) 0.856655 0.972696 0.856655 1.000000 1.028070 0.023390 inf
1956 (PO61_0, PO78_0, PO76_0) (PO57_0) 0.866894 0.977816 0.866894 1.000000 1.022688 0.019231 inf
3590 (PO79_0, PO81_0, PO57_0, PO82_0) (PO78_0) 0.940273 0.977816 0.940273 1.000000 1.022688 0.020859 inf
1942 (PO79_0, PO78_0, PO81_0) (PO60_0) 0.965870 0.994881 0.965870 1.000000 1.005146 0.004945 inf
1943 (PO79_0, PO78_0, PO60_0) (PO81_0) 0.965870 0.994881 0.965870 1.000000 1.005146 0.004945 inf
4436 (PO79_0, PO81_0, PO82_0) (PO61_0, PO60_0) 0.962457 0.969283 0.962457 1.000000 1.031690 0.029564 inf
4817 (PO78_0, PO81_0, PO76_0, PO60_0) (PO57_0) 0.889078 0.977816 0.889078 1.000000 1.022688 0.019724 inf
5695 (PO78_0, PO79_0, PO47_6, PO81_0, PO82_0) (PO61_0) 0.674061 0.972696 0.674061 1.000000 1.028070 0.018404 inf
1958 (PO61_0, PO57_0, PO76_0) (PO78_0) 0.866894 0.977816 0.866894 1.000000 1.022688 0.019231 inf
5649 (PO57_0, PO81_0, PO47_6, PO82_0) (PO78_0, PO60_0) 0.675768 0.972696 0.675768 1.000000 1.028070 0.018451 inf
7679 (PO78_0, PO79_0, PO60_0, PO76_0, PO82_0) (PO61_0) 0.856655 0.972696 0.856655 1.000000 1.028070 0.023390 inf
1970 (PO61_0, PO79_0, PO78_0) (PO57_0) 0.943686 0.977816 0.943686 1.000000 1.022688 0.020935 inf
1971 (PO61_0, PO79_0, PO57_0) (PO78_0) 0.943686 0.977816 0.943686 1.000000 1.022688 0.020935 inf
5754 (PO78_0, PO61_0, PO47_6, PO60_0, PO81_0) (PO82_0) 0.675768 0.972696 0.675768 1.000000 1.028070 0.018451 inf
4435 (PO79_0, PO82_0, PO60_0) (PO61_0, PO81_0) 0.962457 0.969283 0.962457 1.000000 1.031690 0.029564 inf
5755 (PO78_0, PO61_0, PO47_6, PO60_0, PO82_0) (PO81_0) 0.675768 0.994881 0.675768 1.000000 1.005146 0.003460 inf
7738 (PO79_0, PO61_0, PO81_0, PO76_0, PO57_0) (PO82_0) 0.856655 0.972696 0.856655 1.000000 1.028070 0.023390 inf
7742 (PO79_0, PO81_0, PO76_0, PO57_0, PO82_0) (PO61_0) 0.856655 0.972696 0.856655 1.000000 1.028070 0.023390 inf
3529 (PO78_0, PO81_0, PO57_0, PO82_0) (PO61_0) 0.947099 0.972696 0.947099 1.000000 1.028070 0.025859 inf
10064 (PO78_0, PO79_0, PO61_0, PO81_0, PO76_0) (PO82_0, PO60_0) 0.856655 0.969283 0.856655 1.000000 1.031690 0.026314 inf
10057 (PO78_0, PO79_0, PO61_0, PO81_0, PO76_0, PO82_0) (PO60_0) 0.856655 0.994881 0.856655 1.000000 1.005146 0.004386 inf
10058 (PO78_0, PO79_0, PO60_0, PO81_0, PO76_0, PO82_0) (PO61_0) 0.856655 0.972696 0.856655 1.000000 1.028070 0.023390 inf
5635 (PO47_6, PO60_0, PO81_0, PO57_0, PO82_0) (PO78_0) 0.675768 0.977816 0.675768 1.000000 1.022688 0.014991 inf
5638 (PO78_0, PO82_0, PO47_6, PO60_0) (PO81_0, PO57_0) 0.675768 0.972696 0.675768 1.000000 1.028070 0.018451 inf
5640 (PO78_0, PO81_0, PO47_6, PO82_0) (PO57_0, PO60_0) 0.675768 0.972696 0.675768 1.000000 1.028070 0.018451 inf
10062 (PO78_0, PO79_0, PO61_0, PO60_0, PO76_0) (PO81_0, PO82_0) 0.856655 0.969283 0.856655 1.000000 1.031690 0.026314 inf
3587 (PO78_0, PO79_0, PO81_0, PO82_0) (PO57_0) 0.940273 0.977816 0.940273 1.000000 1.022688 0.020859 inf
2956 (PO61_0, PO57_0, PO47_6, PO60_0) (PO82_0) 0.675768 0.972696 0.675768 1.000000 1.028070 0.018451 inf
1928 (PO78_0, PO80_1, PO81_0) (PO60_0) 0.648464 0.994881 0.648464 1.000000 1.005146 0.003320 inf
10069 (PO78_0, PO79_0, PO60_0, PO76_0, PO82_0) (PO61_0, PO81_0) 0.856655 0.969283 0.856655 1.000000 1.031690 0.026314 inf
10070 (PO78_0, PO79_0, PO81_0, PO76_0, PO82_0) (PO61_0, PO60_0) 0.856655 0.969283 0.856655 1.000000 1.031690 0.026314 inf
1929 (PO78_0, PO80_1, PO60_0) (PO81_0) 0.648464 0.994881 0.648464 1.000000 1.005146 0.003320 inf
2960 (PO57_0, PO82_0, PO47_6, PO60_0) (PO61_0) 0.675768 0.972696 0.675768 1.000000 1.028070 0.018451 inf
5648 (PO57_0, PO82_0, PO47_6, PO60_0) (PO78_0, PO81_0) 0.675768 0.972696 0.675768 1.000000 1.028070 0.018451 inf
2929 (PO79_0, PO57_0, PO47_6, PO82_0) (PO61_0) 0.675768 0.972696 0.675768 1.000000 1.028070 0.018451 inf
7676 (PO78_0, PO79_0, PO61_0, PO60_0, PO76_0) (PO82_0) 0.856655 0.972696 0.856655 1.000000 1.028070 0.023390 inf
4432 (PO61_0, PO79_0, PO81_0) (PO82_0, PO60_0) 0.962457 0.969283 0.962457 1.000000 1.031690 0.029564 inf
1998 (PO79_0, PO78_0, PO76_0) (PO57_0) 0.887372 0.977816 0.887372 1.000000 1.022688 0.019686 inf
5760 (PO61_0, PO78_0, PO47_6, PO60_0) (PO81_0, PO82_0) 0.675768 0.969283 0.675768 1.000000 1.031690 0.020757 inf
10184 (PO78_0, PO79_0, PO60_0, PO76_0, PO57_0, PO82_0) (PO61_0) 0.856655 0.972696 0.856655 1.000000 1.028070 0.023390 inf
5761 (PO61_0, PO78_0, PO81_0, PO47_6) (PO82_0, PO60_0) 0.675768 0.969283 0.675768 1.000000 1.031690 0.020757 inf
3625 (PO78_0, PO81_0, PO82_0) (PO57_0, PO60_0) 0.947099 0.972696 0.947099 1.000000 1.028070 0.025859 inf
5767 (PO78_0, PO82_0, PO47_6, PO60_0) (PO61_0, PO81_0) 0.675768 0.969283 0.675768 1.000000 1.031690 0.020757 inf
5768 (PO78_0, PO81_0, PO47_6, PO82_0) (PO61_0, PO60_0) 0.675768 0.969283 0.675768 1.000000 1.031690 0.020757 inf
573 (PO79_0, PO60_0) (PO81_0) 0.988055 0.994881 0.988055 1.000000 1.005146 0.005058 inf
4431 (PO61_0, PO79_0, PO60_0) (PO81_0, PO82_0) 0.962457 0.969283 0.962457 1.000000 1.031690 0.029564 inf
572 (PO79_0, PO81_0) (PO60_0) 0.988055 0.994881 0.988055 1.000000 1.005146 0.005058 inf
10186 (PO79_0, PO61_0, PO60_0, PO76_0, PO57_0, PO82_0) (PO78_0) 0.856655 0.977816 0.856655 1.000000 1.022688 0.019004 inf
10188 (PO78_0, PO79_0, PO61_0, PO60_0, PO76_0) (PO57_0, PO82_0) 0.856655 0.950512 0.856655 1.000000 1.052065 0.042394 inf
2000 (PO79_0, PO57_0, PO76_0) (PO78_0) 0.887372 0.977816 0.887372 1.000000 1.022688 0.019686 inf
567 (PO80_1, PO60_0) (PO81_0) 0.667235 0.994881 0.667235 1.000000 1.005146 0.003416 inf
566 (PO80_1, PO81_0) (PO60_0) 0.667235 0.994881 0.667235 1.000000 1.005146 0.003416 inf
10182 (PO78_0, PO79_0, PO61_0, PO60_0, PO76_0, PO82_0) (PO57_0) 0.856655 0.977816 0.856655 1.000000 1.022688 0.019004 inf
10180 (PO78_0, PO79_0, PO61_0, PO60_0, PO57_0, PO76_0) (PO82_0) 0.856655 0.972696 0.856655 1.000000 1.028070 0.023390 inf
3623 (PO78_0, PO82_0, PO60_0) (PO81_0, PO57_0) 0.947099 0.972696 0.947099 1.000000 1.028070 0.025859 inf
1986 (PO61_0, PO57_0, PO60_0) (PO78_0) 0.947099 0.977816 0.947099 1.000000 1.022688 0.021011 inf
3620 (PO82_0, PO81_0, PO57_0, PO60_0) (PO78_0) 0.947099 0.977816 0.947099 1.000000 1.022688 0.021011 inf
3619 (PO78_0, PO81_0, PO57_0, PO82_0) (PO60_0) 0.947099 0.994881 0.947099 1.000000 1.005146 0.004849 inf
3618 (PO78_0, PO82_0, PO57_0, PO60_0) (PO81_0) 0.947099 0.994881 0.947099 1.000000 1.005146 0.004849 inf
3617 (PO78_0, PO82_0, PO81_0, PO60_0) (PO57_0) 0.947099 0.977816 0.947099 1.000000 1.022688 0.021011 inf
1985 (PO61_0, PO78_0, PO60_0) (PO57_0) 0.947099 0.977816 0.947099 1.000000 1.022688 0.021011 inf
4419 (PO80_1, PO82_0) (PO61_0, PO81_0, PO60_0) 0.653584 0.969283 0.653584 1.000000 1.031690 0.020076 inf
4426 (PO61_0, PO79_0, PO81_0, PO60_0) (PO82_0) 0.962457 0.972696 0.962457 1.000000 1.028070 0.026279 inf
4427 (PO61_0, PO79_0, PO82_0, PO60_0) (PO81_0) 0.962457 0.994881 0.962457 1.000000 1.005146 0.004927 inf
4428 (PO61_0, PO79_0, PO81_0, PO82_0) (PO60_0) 0.962457 0.994881 0.962457 1.000000 1.005146 0.004927 inf
5758 (PO78_0, PO47_6, PO60_0, PO81_0, PO82_0) (PO61_0) 0.675768 0.972696 0.675768 1.000000 1.028070 0.018451 inf
4429 (PO79_0, PO82_0, PO81_0, PO60_0) (PO61_0) 0.962457 0.972696 0.962457 1.000000 1.028070 0.026279 inf
2926 (PO61_0, PO79_0, PO57_0, PO47_6) (PO82_0) 0.675768 0.972696 0.675768 1.000000 1.028070 0.018451 inf
5756 (PO78_0, PO61_0, PO47_6, PO81_0, PO82_0) (PO60_0) 0.675768 0.994881 0.675768 1.000000 1.005146 0.003460 inf
10056 (PO78_0, PO79_0, PO61_0, PO60_0, PO76_0, PO82_0) (PO81_0) 0.856655 0.994881 0.856655 1.000000 1.005146 0.004386 inf
644 (PO61_0, PO80_1) (PO60_0) 0.653584 0.994881 0.653584 1.000000 1.005146 0.003346 inf
10054 (PO78_0, PO79_0, PO61_0, PO60_0, PO81_0, PO76_0) (PO82_0) 0.856655 0.972696 0.856655 1.000000 1.028070 0.023390 inf
5506 (PO78_0, PO61_0, PO47_6, PO81_0, PO57_0) (PO82_0) 0.675768 0.972696 0.675768 1.000000 1.028070 0.018451 inf
3019 (PO79_0, PO82_0, PO47_6, PO60_0) (PO61_0) 0.692833 0.972696 0.692833 1.000000 1.028070 0.018917 inf
3016 (PO61_0, PO79_0, PO47_6, PO60_0) (PO82_0) 0.692833 0.972696 0.692833 1.000000 1.028070 0.018917 inf
4487 (PO79_0, PO82_0, PO80_1, PO60_0) (PO81_0) 0.648464 0.994881 0.648464 1.000000 1.005146 0.003320 inf
7871 (PO61_0, PO79_0, PO81_0, PO57_0) (PO82_0, PO60_0) 0.940273 0.969283 0.940273 1.000000 1.031690 0.028882 inf
7869 (PO61_0, PO79_0, PO57_0, PO60_0) (PO81_0, PO82_0) 0.940273 0.969283 0.940273 1.000000 1.031690 0.028882 inf
699 (PO78_0, PO47_6, PO82_0) (PO61_0) 0.677474 0.972696 0.677474 1.000000 1.028070 0.018498 inf
5507 (PO78_0, PO61_0, PO47_6, PO81_0, PO82_0) (PO57_0) 0.675768 0.977816 0.675768 1.000000 1.022688 0.014991 inf
7865 (PO79_0, PO61_0, PO81_0, PO57_0, PO82_0) (PO60_0) 0.940273 0.994881 0.940273 1.000000 1.005146 0.004814 inf
696 (PO61_0, PO78_0, PO47_6) (PO82_0) 0.677474 0.972696 0.677474 1.000000 1.028070 0.018498 inf
3558 (PO78_0, PO81_0, PO76_0, PO82_0) (PO57_0) 0.863481 0.977816 0.863481 1.000000 1.022688 0.019156 inf
3560 (PO76_0, PO81_0, PO57_0, PO82_0) (PO78_0) 0.863481 0.977816 0.863481 1.000000 1.022688 0.019156 inf
5510 (PO78_0, PO47_6, PO81_0, PO57_0, PO82_0) (PO61_0) 0.675768 0.972696 0.675768 1.000000 1.028070 0.018451 inf
7866 (PO79_0, PO60_0, PO81_0, PO57_0, PO82_0) (PO61_0) 0.940273 0.972696 0.940273 1.000000 1.028070 0.025673 inf
4459 (PO79_0, PO81_0, PO76_0, PO82_0) (PO60_0) 0.877133 0.994881 0.877133 1.000000 1.005146 0.004490 inf
7876 (PO79_0, PO82_0, PO57_0, PO60_0) (PO61_0, PO81_0) 0.940273 0.969283 0.940273 1.000000 1.031690 0.028882 inf
7877 (PO79_0, PO81_0, PO57_0, PO82_0) (PO61_0, PO60_0) 0.940273 0.969283 0.940273 1.000000 1.031690 0.028882 inf
4489 (PO79_0, PO80_1, PO81_0, PO82_0) (PO60_0) 0.648464 0.994881 0.648464 1.000000 1.005146 0.003320 inf
3540 (PO81_0, PO57_0, PO82_0) (PO61_0, PO78_0) 0.947099 0.950512 0.947099 1.000000 1.052065 0.046870 inf
1839 (PO57_0, PO60_0) (PO78_0, PO81_0) 0.972696 0.972696 0.972696 1.000000 1.028070 0.026558 inf
1837 (PO81_0, PO57_0) (PO78_0, PO60_0) 0.972696 0.972696 0.972696 1.000000 1.028070 0.026558 inf
1836 (PO78_0, PO60_0) (PO81_0, PO57_0) 0.972696 0.972696 0.972696 1.000000 1.028070 0.026558 inf
3537 (PO61_0, PO81_0, PO57_0) (PO78_0, PO82_0) 0.947099 0.950512 0.947099 1.000000 1.052065 0.046870 inf
1834 (PO78_0, PO81_0) (PO57_0, PO60_0) 0.972696 0.972696 0.972696 1.000000 1.028070 0.026558 inf
3535 (PO78_0, PO81_0, PO82_0) (PO61_0, PO57_0) 0.947099 0.950512 0.947099 1.000000 1.052065 0.046870 inf
1833 (PO81_0, PO57_0, PO60_0) (PO78_0) 0.972696 0.977816 0.972696 1.000000 1.022688 0.021579 inf
1832 (PO78_0, PO57_0, PO60_0) (PO81_0) 0.972696 0.994881 0.972696 1.000000 1.005146 0.004980 inf
1831 (PO78_0, PO81_0, PO60_0) (PO57_0) 0.972696 0.977816 0.972696 1.000000 1.022688 0.021579 inf
1830 (PO78_0, PO81_0, PO57_0) (PO60_0) 0.972696 0.994881 0.972696 1.000000 1.005146 0.004980 inf
3531 (PO61_0, PO78_0, PO81_0) (PO57_0, PO82_0) 0.947099 0.950512 0.947099 1.000000 1.052065 0.046870 inf
5452 (PO61_0, PO79_0, PO80_1) (PO81_0, PO60_0) 0.648464 0.994881 0.648464 1.000000 1.005146 0.003320 inf
3530 (PO61_0, PO81_0, PO57_0, PO82_0) (PO78_0) 0.947099 0.977816 0.947099 1.000000 1.022688 0.021011 inf
9929 (PO78_0, PO79_0, PO60_0, PO81_0, PO76_0, PO82_0) (PO57_0) 0.856655 0.977816 0.856655 1.000000 1.022688 0.019004 inf
9931 (PO78_0, PO79_0, PO60_0, PO57_0, PO76_0, PO82_0) (PO81_0) 0.856655 0.994881 0.856655 1.000000 1.005146 0.004386 inf
5633 (PO78_0, PO47_6, PO81_0, PO57_0, PO82_0) (PO60_0) 0.675768 0.994881 0.675768 1.000000 1.005146 0.003460 inf
7809 (PO61_0, PO76_0, PO57_0, PO60_0) (PO81_0, PO82_0) 0.863481 0.969283 0.863481 1.000000 1.031690 0.026523 inf
5569 (PO78_0, PO79_0, PO47_6, PO81_0, PO82_0) (PO57_0) 0.674061 0.977816 0.674061 1.000000 1.022688 0.014954 inf
... ... ... ... ... ... ... ... ... ...
4889 (PO81_0, PO57_0, PO60_0) (PO78_0, PO80_1) 0.972696 0.650171 0.648464 0.666667 1.025372 0.016046 1.049488
2225 (PO81_0, PO57_0, PO60_0) (PO80_1) 0.972696 0.668942 0.648464 0.666667 0.996599 -0.002213 0.993174
8456 (PO79_0, PO81_0, PO57_0) (PO78_0, PO80_1, PO60_0) 0.965870 0.648464 0.643345 0.666078 1.027162 0.017012 1.052748
5218 (PO79_0, PO57_0, PO60_0) (PO78_0, PO80_1) 0.965870 0.650171 0.643345 0.666078 1.024466 0.015364 1.047637
5207 (PO78_0, PO79_0, PO57_0, PO60_0) (PO80_1) 0.965870 0.668942 0.643345 0.666078 0.995718 -0.002766 0.991422
4848 (PO78_0, PO79_0, PO81_0, PO57_0) (PO80_1) 0.965870 0.668942 0.643345 0.666078 0.995718 -0.002766 0.991422
8422 (PO78_0, PO79_0, PO60_0, PO81_0, PO57_0) (PO80_1) 0.965870 0.668942 0.643345 0.666078 0.995718 -0.002766 0.991422
5063 (PO78_0, PO79_0, PO81_0) (PO80_1, PO60_0) 0.965870 0.667235 0.643345 0.666078 0.998265 -0.001118 0.996533
4852 (PO78_0, PO79_0, PO81_0) (PO80_1, PO57_0) 0.965870 0.650171 0.643345 0.666078 1.024466 0.015364 1.047637
4859 (PO79_0, PO81_0, PO57_0) (PO78_0, PO80_1) 0.965870 0.650171 0.643345 0.666078 1.024466 0.015364 1.047637
5396 (PO79_0, PO81_0, PO57_0) (PO80_1, PO60_0) 0.965870 0.667235 0.643345 0.666078 0.998265 -0.001118 0.996533
5393 (PO79_0, PO57_0, PO60_0) (PO80_1, PO81_0) 0.965870 0.667235 0.643345 0.666078 0.998265 -0.001118 0.996533
2210 (PO79_0, PO81_0, PO57_0) (PO80_1) 0.965870 0.668942 0.643345 0.666078 0.995718 -0.002766 0.991422
2126 (PO79_0, PO78_0, PO60_0) (PO80_1) 0.965870 0.668942 0.643345 0.666078 0.995718 -0.002766 0.991422
8443 (PO78_0, PO79_0, PO81_0) (PO80_1, PO57_0, PO60_0) 0.965870 0.648464 0.643345 0.666078 1.027162 0.017012 1.052748
5388 (PO79_0, PO81_0, PO57_0, PO60_0) (PO80_1) 0.965870 0.668942 0.643345 0.666078 0.995718 -0.002766 0.991422
8441 (PO78_0, PO79_0, PO60_0) (PO80_1, PO81_0, PO57_0) 0.965870 0.648464 0.643345 0.666078 1.027162 0.017012 1.052748
8438 (PO79_0, PO81_0, PO57_0, PO60_0) (PO78_0, PO80_1) 0.965870 0.650171 0.643345 0.666078 1.024466 0.015364 1.047637
2406 (PO79_0, PO57_0, PO60_0) (PO80_1) 0.965870 0.668942 0.643345 0.666078 0.995718 -0.002766 0.991422
1916 (PO79_0, PO78_0, PO81_0) (PO80_1) 0.965870 0.668942 0.643345 0.666078 0.995718 -0.002766 0.991422
8453 (PO79_0, PO57_0, PO60_0) (PO78_0, PO80_1, PO81_0) 0.965870 0.648464 0.643345 0.666078 1.027162 0.017012 1.052748
5211 (PO78_0, PO79_0, PO60_0) (PO80_1, PO57_0) 0.965870 0.650171 0.643345 0.666078 1.024466 0.015364 1.047637
8431 (PO78_0, PO79_0, PO81_0, PO57_0) (PO80_1, PO60_0) 0.965870 0.667235 0.643345 0.666078 0.998265 -0.001118 0.996533
8428 (PO78_0, PO79_0, PO57_0, PO60_0) (PO80_1, PO81_0) 0.965870 0.667235 0.643345 0.666078 0.998265 -0.001118 0.996533
8427 (PO78_0, PO79_0, PO81_0, PO60_0) (PO80_1, PO57_0) 0.965870 0.650171 0.643345 0.666078 1.024466 0.015364 1.047637
5057 (PO78_0, PO79_0, PO81_0, PO60_0) (PO80_1) 0.965870 0.668942 0.643345 0.666078 0.995718 -0.002766 0.991422
5061 (PO78_0, PO79_0, PO60_0) (PO80_1, PO81_0) 0.965870 0.667235 0.643345 0.666078 0.998265 -0.001118 0.996533
2346 (PO81_0) (PO79_0, PO80_1, PO60_0) 0.994881 0.662116 0.662116 0.665523 1.005146 0.003390 1.010186
2347 (PO60_0) (PO79_0, PO80_1, PO81_0) 0.994881 0.662116 0.662116 0.665523 1.005146 0.003390 1.010186
2343 (PO81_0, PO60_0) (PO79_0, PO80_1) 0.994881 0.663823 0.662116 0.665523 1.002562 0.001692 1.005084
565 (PO81_0) (PO79_0, PO80_1) 0.994881 0.663823 0.662116 0.665523 1.002562 0.001692 1.005084
667 (PO60_0) (PO79_0, PO80_1) 0.994881 0.663823 0.662116 0.665523 1.002562 0.001692 1.005084
63 (PO57_0) (PO80_1) 0.977816 0.668942 0.650171 0.664921 0.993990 -0.003931 0.988001
40 (PO78_0) (PO80_1) 0.977816 0.668942 0.650171 0.664921 0.993990 -0.003931 0.988001
431 (PO78_0) (PO80_1, PO57_0) 0.977816 0.650171 0.650171 0.664921 1.022688 0.014424 1.044022
433 (PO57_0) (PO78_0, PO80_1) 0.977816 0.650171 0.650171 0.664921 1.022688 0.014424 1.044022
429 (PO78_0, PO57_0) (PO80_1) 0.977816 0.668942 0.650171 0.664921 0.993990 -0.003931 0.988001
2032 (PO79_0, PO78_0) (PO80_1, PO57_0) 0.970990 0.650171 0.645051 0.664323 1.021768 0.013742 1.042162
609 (PO79_0, PO57_0) (PO80_1) 0.970990 0.668942 0.645051 0.664323 0.993096 -0.004485 0.986241
2031 (PO79_0, PO57_0) (PO78_0, PO80_1) 0.970990 0.650171 0.645051 0.664323 1.021768 0.013742 1.042162
2028 (PO79_0, PO78_0, PO57_0) (PO80_1) 0.970990 0.668942 0.645051 0.664323 0.993096 -0.004485 0.986241
477 (PO79_0, PO78_0) (PO80_1) 0.970990 0.668942 0.645051 0.664323 0.993096 -0.004485 0.986241
2234 (PO57_0) (PO80_1, PO81_0, PO60_0) 0.977816 0.667235 0.648464 0.663176 0.993916 -0.003969 0.987949
1808 (PO78_0, PO57_0) (PO80_1, PO81_0) 0.977816 0.667235 0.648464 0.663176 0.993916 -0.003969 0.987949
1812 (PO78_0) (PO80_1, PO81_0, PO57_0) 0.977816 0.648464 0.648464 0.663176 1.022688 0.014386 1.043679
2045 (PO78_0, PO57_0) (PO80_1, PO60_0) 0.977816 0.667235 0.648464 0.663176 0.993916 -0.003969 0.987949
4894 (PO78_0, PO57_0) (PO80_1, PO81_0, PO60_0) 0.977816 0.667235 0.648464 0.663176 0.993916 -0.003969 0.987949
618 (PO57_0) (PO80_1, PO60_0) 0.977816 0.667235 0.648464 0.663176 0.993916 -0.003969 0.987949
401 (PO78_0) (PO80_1, PO81_0) 0.977816 0.667235 0.648464 0.663176 0.993916 -0.003969 0.987949
4905 (PO57_0) (PO78_0, PO80_1, PO81_0, PO60_0) 0.977816 0.648464 0.648464 0.663176 1.022688 0.014386 1.043679
485 (PO78_0) (PO80_1, PO60_0) 0.977816 0.667235 0.648464 0.663176 0.993916 -0.003969 0.987949
1938 (PO78_0) (PO80_1, PO81_0, PO60_0) 0.977816 0.667235 0.648464 0.663176 0.993916 -0.003969 0.987949
511 (PO57_0) (PO80_1, PO81_0) 0.977816 0.667235 0.648464 0.663176 0.993916 -0.003969 0.987949
1815 (PO57_0) (PO78_0, PO80_1, PO81_0) 0.977816 0.648464 0.648464 0.663176 1.022688 0.014386 1.043679
2052 (PO57_0) (PO78_0, PO80_1, PO60_0) 0.977816 0.648464 0.648464 0.663176 1.022688 0.014386 1.043679
2050 (PO78_0) (PO80_1, PO57_0, PO60_0) 0.977816 0.648464 0.648464 0.663176 1.022688 0.014386 1.043679
4901 (PO78_0) (PO80_1, PO81_0, PO57_0, PO60_0) 0.977816 0.648464 0.648464 0.663176 1.022688 0.014386 1.043679
2129 (PO79_0, PO78_0) (PO80_1, PO60_0) 0.970990 0.667235 0.643345 0.662566 0.993002 -0.004534 0.986161
4867 (PO79_0, PO57_0) (PO78_0, PO80_1, PO81_0) 0.970990 0.648464 0.643345 0.662566 1.021746 0.013693 1.041791
4861 (PO78_0, PO79_0) (PO80_1, PO81_0, PO57_0) 0.970990 0.648464 0.643345 0.662566 1.021746 0.013693 1.041791
8444 (PO78_0, PO79_0, PO57_0) (PO80_1, PO81_0, PO60_0) 0.970990 0.667235 0.643345 0.662566 0.993002 -0.004534 0.986161
2214 (PO79_0, PO57_0) (PO80_1, PO81_0) 0.970990 0.667235 0.643345 0.662566 0.993002 -0.004534 0.986161
4853 (PO78_0, PO79_0, PO57_0) (PO80_1, PO81_0) 0.970990 0.667235 0.643345 0.662566 0.993002 -0.004534 0.986161
5404 (PO79_0, PO57_0) (PO80_1, PO81_0, PO60_0) 0.970990 0.667235 0.643345 0.662566 0.993002 -0.004534 0.986161
2409 (PO79_0, PO57_0) (PO80_1, PO60_0) 0.970990 0.667235 0.643345 0.662566 0.993002 -0.004534 0.986161
5213 (PO78_0, PO79_0, PO57_0) (PO80_1, PO60_0) 0.970990 0.667235 0.643345 0.662566 0.993002 -0.004534 0.986161
5221 (PO78_0, PO79_0) (PO80_1, PO57_0, PO60_0) 0.970990 0.648464 0.643345 0.662566 1.021746 0.013693 1.041791
8469 (PO79_0, PO57_0) (PO78_0, PO80_1, PO81_0, PO60_0) 0.970990 0.648464 0.643345 0.662566 1.021746 0.013693 1.041791
5227 (PO79_0, PO57_0) (PO78_0, PO80_1, PO60_0) 0.970990 0.648464 0.643345 0.662566 1.021746 0.013693 1.041791
5071 (PO78_0, PO79_0) (PO80_1, PO81_0, PO60_0) 0.970990 0.667235 0.643345 0.662566 0.993002 -0.004534 0.986161
1920 (PO79_0, PO78_0) (PO80_1, PO81_0) 0.970990 0.667235 0.643345 0.662566 0.993002 -0.004534 0.986161
8461 (PO78_0, PO79_0) (PO80_1, PO81_0, PO57_0, PO60_0) 0.970990 0.648464 0.643345 0.662566 1.021746 0.013693 1.041791
8464 (PO78_0, PO81_0) (PO79_0, PO80_1, PO57_0, PO60_0) 0.972696 0.643345 0.643345 0.661404 1.028070 0.017566 1.053334
4863 (PO78_0, PO81_0) (PO79_0, PO80_1, PO57_0) 0.972696 0.645051 0.643345 0.661404 1.025350 0.015906 1.048294
5399 (PO81_0, PO57_0, PO60_0) (PO79_0, PO80_1) 0.972696 0.663823 0.643345 0.661404 0.996356 -0.002353 0.992856
8462 (PO78_0, PO60_0) (PO79_0, PO80_1, PO81_0, PO57_0) 0.972696 0.643345 0.643345 0.661404 1.028070 0.017566 1.053334
5222 (PO78_0, PO60_0) (PO79_0, PO80_1, PO57_0) 0.972696 0.645051 0.643345 0.661404 1.025350 0.015906 1.048294
8475 (PO81_0, PO57_0) (PO78_0, PO79_0, PO80_1, PO60_0) 0.972696 0.643345 0.643345 0.661404 1.028070 0.017566 1.053334
4856 (PO78_0, PO81_0, PO57_0) (PO79_0, PO80_1) 0.972696 0.663823 0.643345 0.661404 0.996356 -0.002353 0.992856
2413 (PO57_0, PO60_0) (PO79_0, PO80_1) 0.972696 0.663823 0.643345 0.661404 0.996356 -0.002353 0.992856
5215 (PO78_0, PO57_0, PO60_0) (PO79_0, PO80_1) 0.972696 0.663823 0.643345 0.661404 0.996356 -0.002353 0.992856
8459 (PO81_0, PO57_0, PO60_0) (PO78_0, PO79_0, PO80_1) 0.972696 0.645051 0.643345 0.661404 1.025350 0.015906 1.048294
5410 (PO81_0, PO57_0) (PO79_0, PO80_1, PO60_0) 0.972696 0.662116 0.643345 0.661404 0.998924 -0.000693 0.997896
5407 (PO57_0, PO60_0) (PO79_0, PO80_1, PO81_0) 0.972696 0.662116 0.643345 0.661404 0.998924 -0.000693 0.997896
8450 (PO78_0, PO81_0, PO57_0) (PO79_0, PO80_1, PO60_0) 0.972696 0.662116 0.643345 0.661404 0.998924 -0.000693 0.997896
2217 (PO81_0, PO57_0) (PO79_0, PO80_1) 0.972696 0.663823 0.643345 0.661404 0.996356 -0.002353 0.992856
5229 (PO57_0, PO60_0) (PO78_0, PO79_0, PO80_1) 0.972696 0.645051 0.643345 0.661404 1.025350 0.015906 1.048294
5074 (PO78_0, PO81_0) (PO79_0, PO80_1, PO60_0) 0.972696 0.662116 0.643345 0.661404 0.998924 -0.000693 0.997896
5065 (PO78_0, PO81_0, PO60_0) (PO79_0, PO80_1) 0.972696 0.663823 0.643345 0.661404 0.996356 -0.002353 0.992856
2133 (PO78_0, PO60_0) (PO79_0, PO80_1) 0.972696 0.663823 0.643345 0.661404 0.996356 -0.002353 0.992856
8446 (PO78_0, PO81_0, PO60_0) (PO79_0, PO80_1, PO57_0) 0.972696 0.645051 0.643345 0.661404 1.025350 0.015906 1.048294
8434 (PO78_0, PO81_0, PO57_0, PO60_0) (PO79_0, PO80_1) 0.972696 0.663823 0.643345 0.661404 0.996356 -0.002353 0.992856
8447 (PO78_0, PO57_0, PO60_0) (PO79_0, PO80_1, PO81_0) 0.972696 0.662116 0.643345 0.661404 0.998924 -0.000693 0.997896
4870 (PO81_0, PO57_0) (PO78_0, PO79_0, PO80_1) 0.972696 0.645051 0.643345 0.661404 1.025350 0.015906 1.048294
5072 (PO78_0, PO60_0) (PO79_0, PO80_1, PO81_0) 0.972696 0.662116 0.643345 0.661404 0.998924 -0.000693 0.997896
1923 (PO78_0, PO81_0) (PO79_0, PO80_1) 0.972696 0.663823 0.643345 0.661404 0.996356 -0.002353 0.992856
8472 (PO57_0, PO60_0) (PO78_0, PO79_0, PO80_1, PO81_0) 0.972696 0.643345 0.643345 0.661404 1.028070 0.017566 1.053334
613 (PO57_0) (PO79_0, PO80_1) 0.977816 0.663823 0.645051 0.659686 0.993768 -0.004045 0.987845
481 (PO78_0) (PO79_0, PO80_1) 0.977816 0.663823 0.645051 0.659686 0.993768 -0.004045 0.987845
2038 (PO57_0) (PO79_0, PO80_1, PO78_0) 0.977816 0.645051 0.645051 0.659686 1.022688 0.014310 1.043003
2039 (PO78_0) (PO79_0, PO80_1, PO57_0) 0.977816 0.645051 0.645051 0.659686 1.022688 0.014310 1.043003
2035 (PO78_0, PO57_0) (PO79_0, PO80_1) 0.977816 0.663823 0.645051 0.659686 0.993768 -0.004045 0.987845
8481 (PO57_0) (PO78_0, PO79_0, PO60_0, PO80_1, PO81_0) 0.977816 0.643345 0.643345 0.657941 1.022688 0.014272 1.042671
5415 (PO57_0) (PO79_0, PO80_1, PO81_0, PO60_0) 0.977816 0.662116 0.643345 0.657941 0.993694 -0.004083 0.987793
1927 (PO78_0) (PO79_0, PO80_1, PO81_0) 0.977816 0.662116 0.643345 0.657941 0.993694 -0.004083 0.987793
2221 (PO57_0) (PO79_0, PO80_1, PO81_0) 0.977816 0.662116 0.643345 0.657941 0.993694 -0.004083 0.987793
2136 (PO78_0) (PO79_0, PO80_1, PO60_0) 0.977816 0.662116 0.643345 0.657941 0.993694 -0.004083 0.987793
8476 (PO78_0) (PO79_0, PO60_0, PO80_1, PO81_0, PO57_0) 0.977816 0.643345 0.643345 0.657941 1.022688 0.014272 1.042671
5231 (PO78_0) (PO79_0, PO80_1, PO57_0, PO60_0) 0.977816 0.643345 0.643345 0.657941 1.022688 0.014272 1.042671
8465 (PO78_0, PO57_0) (PO79_0, PO80_1, PO81_0, PO60_0) 0.977816 0.662116 0.643345 0.657941 0.993694 -0.004083 0.987793
4864 (PO78_0, PO57_0) (PO79_0, PO80_1, PO81_0) 0.977816 0.662116 0.643345 0.657941 0.993694 -0.004083 0.987793
5224 (PO78_0, PO57_0) (PO79_0, PO80_1, PO60_0) 0.977816 0.662116 0.643345 0.657941 0.993694 -0.004083 0.987793
4871 (PO78_0) (PO79_0, PO80_1, PO81_0, PO57_0) 0.977816 0.643345 0.643345 0.657941 1.022688 0.014272 1.042671
4875 (PO57_0) (PO78_0, PO79_0, PO80_1, PO81_0) 0.977816 0.643345 0.643345 0.657941 1.022688 0.014272 1.042671
5235 (PO57_0) (PO78_0, PO79_0, PO80_1, PO60_0) 0.977816 0.643345 0.643345 0.657941 1.022688 0.014272 1.042671
5081 (PO78_0) (PO79_0, PO80_1, PO81_0, PO60_0) 0.977816 0.662116 0.643345 0.657941 0.993694 -0.004083 0.987793
2416 (PO57_0) (PO79_0, PO80_1, PO60_0) 0.977816 0.662116 0.643345 0.657941 0.993694 -0.004083 0.987793
1492 (PO81_0) (PO61_0, PO80_1, PO82_0) 0.994881 0.653584 0.653584 0.656947 1.005146 0.003346 1.009804
2305 (PO60_0) (PO61_0, PO80_1, PO81_0) 0.994881 0.653584 0.653584 0.656947 1.005146 0.003346 1.009804
2304 (PO81_0) (PO61_0, PO80_1, PO60_0) 0.994881 0.653584 0.653584 0.656947 1.005146 0.003346 1.009804
649 (PO60_0) (PO61_0, PO80_1) 0.994881 0.653584 0.653584 0.656947 1.005146 0.003346 1.009804
372 (PO60_0) (PO80_1, PO82_0) 0.994881 0.653584 0.653584 0.656947 1.005146 0.003346 1.009804
2301 (PO81_0, PO60_0) (PO61_0, PO80_1) 0.994881 0.653584 0.653584 0.656947 1.005146 0.003346 1.009804
288 (PO81_0) (PO80_1, PO82_0) 0.994881 0.653584 0.653584 0.656947 1.005146 0.003346 1.009804
4424 (PO81_0) (PO61_0, PO82_0, PO80_1, PO60_0) 0.994881 0.653584 0.653584 0.656947 1.005146 0.003346 1.009804
4422 (PO60_0) (PO61_0, PO80_1, PO81_0, PO82_0) 0.994881 0.653584 0.653584 0.656947 1.005146 0.003346 1.009804
1730 (PO60_0) (PO61_0, PO80_1, PO82_0) 0.994881 0.653584 0.653584 0.656947 1.005146 0.003346 1.009804
1569 (PO81_0, PO60_0) (PO80_1, PO82_0) 0.994881 0.653584 0.653584 0.656947 1.005146 0.003346 1.009804
535 (PO81_0) (PO61_0, PO80_1) 0.994881 0.653584 0.653584 0.656947 1.005146 0.003346 1.009804
1574 (PO60_0) (PO80_1, PO81_0, PO82_0) 0.994881 0.653584 0.653584 0.656947 1.005146 0.003346 1.009804
1576 (PO81_0) (PO82_0, PO80_1, PO60_0) 0.994881 0.653584 0.653584 0.656947 1.005146 0.003346 1.009804
4416 (PO81_0, PO60_0) (PO61_0, PO80_1, PO82_0) 0.994881 0.653584 0.653584 0.656947 1.005146 0.003346 1.009804
4682 (PO79_0, PO60_0) (PO61_0, PO80_1, PO82_0) 0.988055 0.653584 0.648464 0.656304 1.004162 0.002688 1.007915
1765 (PO79_0, PO60_0) (PO80_1, PO82_0) 0.988055 0.653584 0.648464 0.656304 1.004162 0.002688 1.007915
2440 (PO79_0, PO60_0) (PO61_0, PO80_1) 0.988055 0.653584 0.648464 0.656304 1.004162 0.002688 1.007915
8092 (PO79_0, PO81_0) (PO61_0, PO82_0, PO80_1, PO60_0) 0.988055 0.653584 0.648464 0.656304 1.004162 0.002688 1.007915
4492 (PO79_0, PO81_0, PO60_0) (PO80_1, PO82_0) 0.988055 0.653584 0.648464 0.656304 1.004162 0.002688 1.007915
8090 (PO79_0, PO60_0) (PO61_0, PO80_1, PO81_0, PO82_0) 0.988055 0.653584 0.648464 0.656304 1.004162 0.002688 1.007915
4503 (PO79_0, PO81_0) (PO82_0, PO80_1, PO60_0) 0.988055 0.653584 0.648464 0.656304 1.004162 0.002688 1.007915
4501 (PO79_0, PO60_0) (PO80_1, PO81_0, PO82_0) 0.988055 0.653584 0.648464 0.656304 1.004162 0.002688 1.007915
4383 (PO79_0, PO81_0) (PO61_0, PO80_1, PO82_0) 0.988055 0.653584 0.648464 0.656304 1.004162 0.002688 1.007915
8074 (PO79_0, PO81_0, PO60_0) (PO61_0, PO80_1, PO82_0) 0.988055 0.653584 0.648464 0.656304 1.004162 0.002688 1.007915
5464 (PO79_0, PO81_0) (PO61_0, PO80_1, PO60_0) 0.988055 0.653584 0.648464 0.656304 1.004162 0.002688 1.007915
5462 (PO79_0, PO60_0) (PO61_0, PO80_1, PO81_0) 0.988055 0.653584 0.648464 0.656304 1.004162 0.002688 1.007915
5455 (PO79_0, PO81_0, PO60_0) (PO61_0, PO80_1) 0.988055 0.653584 0.648464 0.656304 1.004162 0.002688 1.007915
1555 (PO79_0, PO81_0) (PO80_1, PO82_0) 0.988055 0.653584 0.648464 0.656304 1.004162 0.002688 1.007915
2286 (PO79_0, PO81_0) (PO61_0, PO80_1) 0.988055 0.653584 0.648464 0.656304 1.004162 0.002688 1.007915
1095 (PO79_0, PO81_0) (PO47_6, PO76_0) 0.988055 0.650171 0.646758 0.654577 1.006777 0.004354 1.012756
3474 (PO79_0, PO81_0, PO60_0) (PO76_0, PO47_6) 0.988055 0.650171 0.646758 0.654577 1.006777 0.004354 1.012756
3482 (PO79_0, PO60_0) (PO76_0, PO81_0, PO47_6) 0.988055 0.648464 0.646758 0.654577 1.009426 0.006040 1.017696
3483 (PO79_0, PO81_0) (PO76_0, PO47_6, PO60_0) 0.988055 0.648464 0.646758 0.654577 1.009426 0.006040 1.017696
1192 (PO79_0, PO60_0) (PO47_6, PO76_0) 0.988055 0.650171 0.646758 0.654577 1.006777 0.004354 1.012756
1560 (PO79_0) (PO80_1, PO81_0, PO82_0) 0.993174 0.653584 0.648464 0.652921 0.998986 -0.000658 0.998091
2289 (PO79_0) (PO61_0, PO80_1, PO81_0) 0.993174 0.653584 0.648464 0.652921 0.998986 -0.000658 0.998091
365 (PO79_0) (PO80_1, PO82_0) 0.993174 0.653584 0.648464 0.652921 0.998986 -0.000658 0.998091
4391 (PO79_0) (PO61_0, PO80_1, PO81_0, PO82_0) 0.993174 0.653584 0.648464 0.652921 0.998986 -0.000658 0.998091
215 (PO79_0) (PO47_6, PO76_0) 0.993174 0.650171 0.648464 0.652921 1.004230 0.002732 1.007924
4691 (PO79_0) (PO61_0, PO82_0, PO80_1, PO60_0) 0.993174 0.653584 0.648464 0.652921 0.998986 -0.000658 0.998091
1715 (PO79_0) (PO61_0, PO80_1, PO82_0) 0.993174 0.653584 0.648464 0.652921 0.998986 -0.000658 0.998091
2443 (PO79_0) (PO61_0, PO80_1, PO60_0) 0.993174 0.653584 0.648464 0.652921 0.998986 -0.000658 0.998091
1770 (PO79_0) (PO82_0, PO80_1, PO60_0) 0.993174 0.653584 0.648464 0.652921 0.998986 -0.000658 0.998091
8104 (PO79_0) (PO61_0, PO60_0, PO80_1, PO81_0, PO82_0) 0.993174 0.653584 0.648464 0.652921 0.998986 -0.000658 0.998091
4511 (PO79_0) (PO82_0, PO80_1, PO81_0, PO60_0) 0.993174 0.653584 0.648464 0.652921 0.998986 -0.000658 0.998091
642 (PO79_0) (PO61_0, PO80_1) 0.993174 0.653584 0.648464 0.652921 0.998986 -0.000658 0.998091
5471 (PO79_0) (PO61_0, PO80_1, PO81_0, PO60_0) 0.993174 0.653584 0.648464 0.652921 0.998986 -0.000658 0.998091
8099 (PO81_0, PO60_0) (PO61_0, PO79_0, PO80_1, PO82_0) 0.994881 0.648464 0.648464 0.651801 1.005146 0.003320 1.009583
510 (PO81_0) (PO80_1, PO57_0) 0.994881 0.650171 0.648464 0.651801 1.002508 0.001622 1.004682
168 (PO81_0) (PO47_6, PO76_0) 0.994881 0.650171 0.648464 0.651801 1.002508 0.001622 1.004682
1941 (PO60_0) (PO78_0, PO80_1, PO81_0) 0.994881 0.648464 0.648464 0.651801 1.005146 0.003320 1.009583
4394 (PO81_0) (PO61_0, PO79_0, PO80_1, PO82_0) 0.994881 0.648464 0.648464 0.651801 1.005146 0.003320 1.009583
2445 (PO60_0) (PO61_0, PO79_0, PO80_1) 0.994881 0.648464 0.648464 0.651801 1.005146 0.003320 1.009583
8108 (PO81_0) (PO79_0, PO61_0, PO60_0, PO80_1, PO82_0) 0.994881 0.648464 0.648464 0.651801 1.005146 0.003320 1.009583
619 (PO60_0) (PO80_1, PO57_0) 0.994881 0.650171 0.648464 0.651801 1.002508 0.001622 1.004682
1937 (PO81_0, PO60_0) (PO78_0, PO80_1) 0.994881 0.650171 0.648464 0.651801 1.002508 0.001622 1.004682
4896 (PO81_0, PO60_0) (PO78_0, PO80_1, PO57_0) 0.994881 0.650171 0.648464 0.651801 1.002508 0.001622 1.004682
4904 (PO81_0) (PO78_0, PO80_1, PO57_0, PO60_0) 0.994881 0.648464 0.648464 0.651801 1.005146 0.003320 1.009583
1940 (PO81_0) (PO78_0, PO80_1, PO60_0) 0.994881 0.648464 0.648464 0.651801 1.005146 0.003320 1.009583
4902 (PO60_0) (PO78_0, PO80_1, PO81_0, PO57_0) 0.994881 0.648464 0.648464 0.651801 1.005146 0.003320 1.009583
223 (PO60_0) (PO47_6, PO76_0) 0.994881 0.650171 0.648464 0.651801 1.002508 0.001622 1.004682
8106 (PO60_0) (PO79_0, PO61_0, PO80_1, PO81_0, PO82_0) 0.994881 0.648464 0.648464 0.651801 1.005146 0.003320 1.009583
2230 (PO81_0, PO60_0) (PO80_1, PO57_0) 0.994881 0.650171 0.648464 0.651801 1.002508 0.001622 1.004682
1562 (PO81_0) (PO79_0, PO80_1, PO82_0) 0.994881 0.648464 0.648464 0.651801 1.005146 0.003320 1.009583
2233 (PO81_0) (PO80_1, PO57_0, PO60_0) 0.994881 0.648464 0.648464 0.651801 1.005146 0.003320 1.009583
4693 (PO60_0) (PO61_0, PO79_0, PO80_1, PO82_0) 0.994881 0.648464 0.648464 0.651801 1.005146 0.003320 1.009583
403 (PO81_0) (PO78_0, PO80_1) 0.994881 0.650171 0.648464 0.651801 1.002508 0.001622 1.004682
2053 (PO60_0) (PO78_0, PO80_1, PO57_0) 0.994881 0.650171 0.648464 0.651801 1.002508 0.001622 1.004682
2291 (PO81_0) (PO61_0, PO79_0, PO80_1) 0.994881 0.648464 0.648464 0.651801 1.005146 0.003320 1.009583
1110 (PO81_0, PO60_0) (PO47_6, PO76_0) 0.994881 0.650171 0.648464 0.651801 1.002508 0.001622 1.004682
1113 (PO81_0) (PO47_6, PO76_0, PO60_0) 0.994881 0.648464 0.648464 0.651801 1.005146 0.003320 1.009583
1115 (PO60_0) (PO47_6, PO81_0, PO76_0) 0.994881 0.648464 0.648464 0.651801 1.005146 0.003320 1.009583
1814 (PO81_0) (PO78_0, PO80_1, PO57_0) 0.994881 0.650171 0.648464 0.651801 1.002508 0.001622 1.004682
1772 (PO60_0) (PO79_0, PO80_1, PO82_0) 0.994881 0.648464 0.648464 0.651801 1.005146 0.003320 1.009583
4514 (PO81_0) (PO79_0, PO82_0, PO80_1, PO60_0) 0.994881 0.648464 0.648464 0.651801 1.005146 0.003320 1.009583
4512 (PO60_0) (PO79_0, PO80_1, PO81_0, PO82_0) 0.994881 0.648464 0.648464 0.651801 1.005146 0.003320 1.009583
5473 (PO60_0) (PO61_0, PO79_0, PO80_1, PO81_0) 0.994881 0.648464 0.648464 0.651801 1.005146 0.003320 1.009583
4506 (PO81_0, PO60_0) (PO79_0, PO80_1, PO82_0) 0.994881 0.648464 0.648464 0.651801 1.005146 0.003320 1.009583
5475 (PO81_0) (PO61_0, PO79_0, PO80_1, PO60_0) 0.994881 0.648464 0.648464 0.651801 1.005146 0.003320 1.009583
2235 (PO60_0) (PO80_1, PO81_0, PO57_0) 0.994881 0.648464 0.648464 0.651801 1.005146 0.003320 1.009583
5469 (PO81_0, PO60_0) (PO61_0, PO79_0, PO80_1) 0.994881 0.648464 0.648464 0.651801 1.005146 0.003320 1.009583
487 (PO60_0) (PO78_0, PO80_1) 0.994881 0.650171 0.648464 0.651801 1.002508 0.001622 1.004682
1099 (PO79_0) (PO47_6, PO81_0, PO76_0) 0.993174 0.648464 0.646758 0.651203 1.004223 0.002720 1.007852
1196 (PO79_0) (PO47_6, PO76_0, PO60_0) 0.993174 0.648464 0.646758 0.651203 1.004223 0.002720 1.007852
3491 (PO79_0) (PO76_0, PO81_0, PO47_6, PO60_0) 0.993174 0.648464 0.646758 0.651203 1.004223 0.002720 1.007852
8466 (PO79_0, PO60_0) (PO78_0, PO80_1, PO81_0, PO57_0) 0.988055 0.648464 0.643345 0.651123 1.004100 0.002627 1.007620
8468 (PO79_0, PO81_0) (PO78_0, PO80_1, PO57_0, PO60_0) 0.988055 0.648464 0.643345 0.651123 1.004100 0.002627 1.007620
2130 (PO79_0, PO60_0) (PO78_0, PO80_1) 0.988055 0.650171 0.643345 0.651123 1.001464 0.000941 1.002729
2410 (PO79_0, PO60_0) (PO80_1, PO57_0) 0.988055 0.650171 0.643345 0.651123 1.001464 0.000941 1.002729
5401 (PO79_0, PO60_0) (PO80_1, PO81_0, PO57_0) 0.988055 0.648464 0.643345 0.651123 1.004100 0.002627 1.007620
5068 (PO79_0, PO81_0, PO60_0) (PO78_0, PO80_1) 0.988055 0.650171 0.643345 0.651123 1.001464 0.000941 1.002729
2213 (PO79_0, PO81_0) (PO80_1, PO57_0) 0.988055 0.650171 0.643345 0.651123 1.001464 0.000941 1.002729
5225 (PO79_0, PO60_0) (PO78_0, PO80_1, PO57_0) 0.988055 0.650171 0.643345 0.651123 1.001464 0.000941 1.002729
1919 (PO79_0, PO81_0) (PO78_0, PO80_1) 0.988055 0.650171 0.643345 0.651123 1.001464 0.000941 1.002729
5075 (PO79_0, PO60_0) (PO78_0, PO80_1, PO81_0) 0.988055 0.648464 0.643345 0.651123 1.004100 0.002627 1.007620
5077 (PO79_0, PO81_0) (PO78_0, PO80_1, PO60_0) 0.988055 0.648464 0.643345 0.651123 1.004100 0.002627 1.007620
4866 (PO79_0, PO81_0) (PO78_0, PO80_1, PO57_0) 0.988055 0.650171 0.643345 0.651123 1.001464 0.000941 1.002729
5403 (PO79_0, PO81_0) (PO80_1, PO57_0, PO60_0) 0.988055 0.648464 0.643345 0.651123 1.004100 0.002627 1.007620
8452 (PO79_0, PO81_0, PO60_0) (PO78_0, PO80_1, PO57_0) 0.988055 0.650171 0.643345 0.651123 1.001464 0.000941 1.002729
5392 (PO79_0, PO81_0, PO60_0) (PO80_1, PO57_0) 0.988055 0.650171 0.643345 0.651123 1.001464 0.000941 1.002729
1100 (PO81_0) (PO79_0, PO47_6, PO76_0) 0.994881 0.648464 0.646758 0.650086 1.002501 0.001613 1.004634
3488 (PO81_0, PO60_0) (PO79_0, PO76_0, PO47_6) 0.994881 0.648464 0.646758 0.650086 1.002501 0.001613 1.004634
1199 (PO60_0) (PO79_0, PO47_6, PO76_0) 0.994881 0.648464 0.646758 0.650086 1.002501 0.001613 1.004634
3493 (PO60_0) (PO79_0, PO76_0, PO81_0, PO47_6) 0.994881 0.646758 0.646758 0.650086 1.005146 0.003311 1.009511
3494 (PO81_0) (PO79_0, PO76_0, PO47_6, PO60_0) 0.994881 0.646758 0.646758 0.650086 1.005146 0.003311 1.009511
611 (PO79_0) (PO80_1, PO57_0) 0.993174 0.650171 0.645051 0.649485 0.998945 -0.000681 0.998043
2036 (PO79_0) (PO78_0, PO80_1, PO57_0) 0.993174 0.650171 0.645051 0.649485 0.998945 -0.000681 0.998043
479 (PO79_0) (PO78_0, PO80_1) 0.993174 0.650171 0.645051 0.649485 0.998945 -0.000681 0.998043
2414 (PO79_0) (PO80_1, PO57_0, PO60_0) 0.993174 0.648464 0.643345 0.647766 0.998924 -0.000693 0.998019
2134 (PO79_0) (PO78_0, PO80_1, PO60_0) 0.993174 0.648464 0.643345 0.647766 0.998924 -0.000693 0.998019
5411 (PO79_0) (PO80_1, PO81_0, PO57_0, PO60_0) 0.993174 0.648464 0.643345 0.647766 0.998924 -0.000693 0.998019
8477 (PO79_0) (PO78_0, PO60_0, PO80_1, PO81_0, PO57_0) 0.993174 0.648464 0.643345 0.647766 0.998924 -0.000693 0.998019
5232 (PO79_0) (PO78_0, PO80_1, PO57_0, PO60_0) 0.993174 0.648464 0.643345 0.647766 0.998924 -0.000693 0.998019
1924 (PO79_0) (PO78_0, PO80_1, PO81_0) 0.993174 0.648464 0.643345 0.647766 0.998924 -0.000693 0.998019
4872 (PO79_0) (PO78_0, PO80_1, PO81_0, PO57_0) 0.993174 0.648464 0.643345 0.647766 0.998924 -0.000693 0.998019
5082 (PO79_0) (PO78_0, PO80_1, PO81_0, PO60_0) 0.993174 0.648464 0.643345 0.647766 0.998924 -0.000693 0.998019
2218 (PO79_0) (PO80_1, PO81_0, PO57_0) 0.993174 0.648464 0.643345 0.647766 0.998924 -0.000693 0.998019
2417 (PO60_0) (PO79_0, PO80_1, PO57_0) 0.994881 0.645051 0.643345 0.646655 1.002487 0.001596 1.004540
1926 (PO81_0) (PO79_0, PO80_1, PO78_0) 0.994881 0.645051 0.643345 0.646655 1.002487 0.001596 1.004540
5083 (PO60_0) (PO78_0, PO79_0, PO80_1, PO81_0) 0.994881 0.643345 0.643345 0.646655 1.005146 0.003294 1.009369
5085 (PO81_0) (PO78_0, PO79_0, PO80_1, PO60_0) 0.994881 0.643345 0.643345 0.646655 1.005146 0.003294 1.009369
2220 (PO81_0) (PO79_0, PO80_1, PO57_0) 0.994881 0.645051 0.643345 0.646655 1.002487 0.001596 1.004540
5414 (PO81_0) (PO79_0, PO80_1, PO57_0, PO60_0) 0.994881 0.643345 0.643345 0.646655 1.005146 0.003294 1.009369
8480 (PO81_0) (PO78_0, PO79_0, PO60_0, PO80_1, PO57_0) 0.994881 0.643345 0.643345 0.646655 1.005146 0.003294 1.009369
5412 (PO60_0) (PO79_0, PO80_1, PO81_0, PO57_0) 0.994881 0.643345 0.643345 0.646655 1.005146 0.003294 1.009369
5079 (PO81_0, PO60_0) (PO78_0, PO79_0, PO80_1) 0.994881 0.645051 0.643345 0.646655 1.002487 0.001596 1.004540
8478 (PO60_0) (PO78_0, PO79_0, PO80_1, PO81_0, PO57_0) 0.994881 0.643345 0.643345 0.646655 1.005146 0.003294 1.009369
8471 (PO81_0, PO60_0) (PO78_0, PO79_0, PO80_1, PO57_0) 0.994881 0.645051 0.643345 0.646655 1.002487 0.001596 1.004540
2137 (PO60_0) (PO79_0, PO80_1, PO78_0) 0.994881 0.645051 0.643345 0.646655 1.002487 0.001596 1.004540
5406 (PO81_0, PO60_0) (PO79_0, PO80_1, PO57_0) 0.994881 0.645051 0.643345 0.646655 1.002487 0.001596 1.004540
5233 (PO60_0) (PO78_0, PO79_0, PO80_1, PO57_0) 0.994881 0.645051 0.643345 0.646655 1.002487 0.001596 1.004540
4874 (PO81_0) (PO78_0, PO79_0, PO80_1, PO57_0) 0.994881 0.645051 0.643345 0.646655 1.002487 0.001596 1.004540

11066 rows × 9 columns

Classement des règles selon le niveau de confiance.

Les règles les plus précises sont: (PO61_0, PO60_0, PO57_0) ===> (PO78_0, PO81_0, PO82_0)

(PO61_0, PO47_6, PO81_0, PO57_0)===> (PO78_0, PO60_0, PO82_0)

(PO60_0, PO57_0, PO82_0)===>(PO78_0, PO61_0, PO81_0)

(PO78_0, PO60_0, PO82_0))===>(PO61_0, PO81_0, PO57_0)

(PO78_0, PO60_0, PO61_0)===>((PO81_0, PO82_0, PO57_0)

PO61_0 : les clients n'ayant pas une Contribution boat policies.

PO60_0: les clients ayant une Contribution surfboard policies égale à 0.

PO57_O: les clients ayant une Contribution family accidents insurance policies égale à 0.

PO78_0: les clients ayant Number of family accidents insurance policies égale à 0.

PO82_0 : les clients n'ayant pas de Contribution surfboard policies.

PO68_1 : les clients ayant un nombre de car policies égale à 1.

PO81_0 : les clients ayant un nombre of surfboard policies égale à 0.

PO61_0: les clients ayant une Contribution boat policies égale à 0.

PO47_6 : les clients ayant un PPERSAUT Contribution car policies égale à 6.

Cet output nous a permis de faire identifier les critères des clients qu’on doit les envoyer des mails .En effet, les clients n’ayant ni de Contribution surfboard policies, ni de Contribution Contribution boat policies , ni de un nombre of surfboard policies, un nombre de car policies qui est égale à 1 et un PPERSAUT Contribution car policies égale à 6 sont les plus intéressés d’avoir une assurance sur caravane.

Les 10 règles les plus « pertinentes »:

In [107]:
import mlxtend.plotting as mlplot
In [108]:
#draw a graph of associations
def draw_graph(rules, rules_to_show):
    import networkx as nx  
    G1 = nx.DiGraph()
   
    color_map=[]
    N = 50
    colors = np.random.rand(N)    
    strs=['R0', 'R1', 'R2', 'R3', 'R4', 'R5', 'R6', 'R7', 'R8','R9']   


    for i in range (rules_to_show):      
        G1.add_nodes_from(["R"+str(i)])
    
     
        for a in rules.iloc[i]["antecedents"]:

            G1.add_nodes_from([a])

            G1.add_edge(a, "R"+str(i), color=colors[i] , weight = 2)

        for c in rules.iloc[i]['consequents']:

            G1.add_nodes_from([c])

            G1.add_edge("R"+str(i), c, color=colors[i],  weight=2)

    for node in G1:
        found_a_string = False
        for item in strs: 
            if node==item:
                found_a_string = True
        if found_a_string:
            color_map.append('blue')
        else:
            color_map.append('red')       
 
 
   
    edges = G1.edges()
    colors = [G1[u][v]['color'] for u,v in edges]
    weights = [G1[u][v]['weight'] for u,v in edges]

    pos = nx.spring_layout(G1, k=16, scale=1)
    nx.draw(G1, pos, edges=edges, node_color = color_map, edge_color=colors, width=weights, font_size=16, with_labels=False)            

    for p in pos: 
        pos[p][1] += 0.07
    nx.draw_networkx_labels(G1, pos)
    plt.show()
In [109]:
draw_graph(rules, 10)

Scoring

In [110]:
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
In [112]:
data_scoring = pd.read_csv('df_reduced.csv', sep='\t')

data_scoring = data_scoring.drop(columns='Unnamed: 0', axis=1)
X = data_scoring.drop(['CLASS'],axis=1)
y = pd.DataFrame(data_scoring.iloc[:,-1])

X.head()
                 
Out[112]:
PO59 PO47 SD1 PO82 PO78 PO81 SD37 SD43 PO44 SD22 PO57 PO61 SD18 SD35 PO76 SD32 PO65 SD7 SD30 SD24 SD38 SD28 SD26 SD15 SD14 SD23 PO80 PO79 SD39 SD10 PO60
0 5 6 33 0 0 0 0 3 0 2 0 0 7 8 0 8 0 5 1 2 4 6 1 6 2 5 1 0 5 7 0
1 2 0 37 0 0 0 2 4 2 5 0 0 4 6 0 7 2 4 2 4 0 5 2 5 4 0 1 0 5 6 0
2 2 6 37 0 0 0 4 4 2 7 0 0 4 9 0 7 1 4 7 2 5 4 5 2 4 0 1 0 0 3 0
3 2 6 9 0 0 0 1 4 0 3 0 0 2 7 0 9 0 3 5 2 5 4 2 4 3 1 1 0 3 5 0
4 6 0 40 0 0 0 0 3 0 0 0 0 0 5 0 6 0 4 4 0 0 0 0 4 4 0 1 0 9 7 0

LightGBM

In [113]:
from lightgbm import LGBMClassifier
import xgboost as xgb
from sklearn.ensemble import RandomForestRegressor
lgbm = LGBMClassifier(objective='binary',class_weight='balanced')
lgbm.fit(X, y)
C:\Users\yassi\Anaconda3\lib\site-packages\sklearn\preprocessing\label.py:219: DataConversionWarning:

A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().

C:\Users\yassi\Anaconda3\lib\site-packages\sklearn\preprocessing\label.py:252: DataConversionWarning:

A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().

Out[113]:
LGBMClassifier(boosting_type='gbdt', class_weight='balanced',
        colsample_bytree=1.0, importance_type='split', learning_rate=0.1,
        max_depth=-1, min_child_samples=20, min_child_weight=0.001,
        min_split_gain=0.0, n_estimators=100, n_jobs=-1, num_leaves=31,
        objective='binary', random_state=None, reg_alpha=0.0,
        reg_lambda=0.0, silent=True, subsample=1.0,
        subsample_for_bin=200000, subsample_freq=0)
In [114]:
y_pred=lgbm.predict(X)
In [115]:
proba=lgbm.predict_proba(X)
In [116]:
proba
Out[116]:
array([[0.52481952, 0.47518048],
       [0.968437  , 0.031563  ],
       [0.90653892, 0.09346108],
       ...,
       [0.55206356, 0.44793644],
       [0.42561745, 0.57438255],
       [0.86828346, 0.13171654]])
In [117]:
pd.DataFrame(proba)
Out[117]:
0 1
0 0.524820 0.475180
1 0.968437 0.031563
2 0.906539 0.093461
3 0.780641 0.219359
4 0.997308 0.002692
5 0.912865 0.087135
6 0.716450 0.283550
7 0.351331 0.648669
8 0.272607 0.727393
9 0.955121 0.044879
10 0.849291 0.150709
11 0.593525 0.406475
12 0.302819 0.697181
13 0.963665 0.036335
14 0.989964 0.010036
15 0.774084 0.225916
16 0.904463 0.095537
17 0.172269 0.827731
18 0.720106 0.279894
19 0.987891 0.012109
20 0.327620 0.672380
21 0.537565 0.462435
22 0.553712 0.446288
23 0.959719 0.040281
24 0.449236 0.550764
25 0.529375 0.470625
26 0.186065 0.813935
27 0.987739 0.012261
28 0.998768 0.001232
29 0.980570 0.019430
30 0.766218 0.233782
31 0.962734 0.037266
32 0.560563 0.439437
33 0.815606 0.184394
34 0.455897 0.544103
35 0.698094 0.301906
36 0.157021 0.842979
37 0.993736 0.006264
38 0.980451 0.019549
39 0.888168 0.111832
40 0.993528 0.006472
41 0.178272 0.821728
42 0.980151 0.019849
43 0.529438 0.470562
44 0.555774 0.444226
45 0.235698 0.764302
46 0.934649 0.065351
47 0.878607 0.121393
48 0.395747 0.604253
49 0.955302 0.044698
50 0.554312 0.445688
51 0.941529 0.058471
52 0.928429 0.071571
53 0.980574 0.019426
54 0.794889 0.205111
55 0.347125 0.652875
56 0.983532 0.016468
57 0.109923 0.890077
58 0.933581 0.066419
59 0.981847 0.018153
60 0.847474 0.152526
61 0.691495 0.308505
62 0.841520 0.158480
63 0.995036 0.004964
64 0.994965 0.005035
65 0.185419 0.814581
66 0.574863 0.425137
67 0.310302 0.689698
68 0.845276 0.154724
69 0.952321 0.047679
70 0.974687 0.025313
71 0.972633 0.027367
72 0.885097 0.114903
73 0.441281 0.558719
74 0.471927 0.528073
75 0.578851 0.421149
76 0.914145 0.085855
77 0.862178 0.137822
78 0.872637 0.127363
79 0.464694 0.535306
80 0.831685 0.168315
81 0.993681 0.006319
82 0.854885 0.145115
83 0.948049 0.051951
84 0.972496 0.027504
85 0.288432 0.711568
86 0.833930 0.166070
87 0.631249 0.368751
88 0.847604 0.152396
89 0.715554 0.284446
90 0.886166 0.113834
91 0.618845 0.381155
92 0.536389 0.463611
93 0.984655 0.015345
94 0.611839 0.388161
95 0.973654 0.026346
96 0.888421 0.111579
97 0.323125 0.676875
98 0.261999 0.738001
99 0.992366 0.007634
100 0.916942 0.083058
101 0.867468 0.132532
102 0.762204 0.237796
103 0.828980 0.171020
104 0.336029 0.663971
105 0.984916 0.015084
106 0.904212 0.095788
107 0.868891 0.131109
108 0.747290 0.252710
109 0.452716 0.547284
110 0.869672 0.130328
111 0.984858 0.015142
112 0.715908 0.284092
113 0.690431 0.309569
114 0.377281 0.622719
115 0.974918 0.025082
116 0.388048 0.611952
117 0.919603 0.080397
118 0.308976 0.691024
119 0.991703 0.008297
120 0.935598 0.064402
121 0.842233 0.157767
122 0.928689 0.071311
123 0.857326 0.142674
124 0.519935 0.480065
125 0.801069 0.198931
126 0.304276 0.695724
127 0.130846 0.869154
128 0.885392 0.114608
129 0.923947 0.076053
130 0.531405 0.468595
131 0.532100 0.467900
132 0.982397 0.017603
133 0.951212 0.048788
134 0.954475 0.045525
135 0.905708 0.094292
136 0.617336 0.382664
137 0.949494 0.050506
138 0.843637 0.156363
139 0.960863 0.039137
140 0.978498 0.021502
141 0.431868 0.568132
142 0.782674 0.217326
143 0.935235 0.064765
144 0.973969 0.026031
145 0.892143 0.107857
146 0.801631 0.198369
147 0.708866 0.291134
148 0.983047 0.016953
149 0.327874 0.672126
150 0.402199 0.597801
151 0.657754 0.342246
152 0.933008 0.066992
153 0.831940 0.168060
154 0.638248 0.361752
155 0.826001 0.173999
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9822 rows × 2 columns

In [118]:
from sklearn.metrics import confusion_matrix,accuracy_score,precision_score,cohen_kappa_score,recall_score,auc
cm = confusion_matrix(y_pred,y)
print(cm)
print('accuracy_score :',accuracy_score(y,y_pred))
print('auc :',auc(y,y_pred,reorder=True))
print('precision_score : ',precision_score(y,y_pred))
print('recall_score :', recall_score(y,y_pred))
print('kappa_score :',cohen_kappa_score(y,y_pred))
[[7874    8]
 [1362  578]]
accuracy_score : 0.8605172062716351
auc : 0.5
precision_score :  0.2979381443298969
recall_score : 0.9863481228668942
kappa_score : 0.4029230199356675
C:\Users\yassi\Anaconda3\lib\site-packages\sklearn\metrics\ranking.py:104: DeprecationWarning:

The 'reorder' parameter has been deprecated in version 0.20 and will be removed in 0.22. It is recommended not to set 'reorder' and ensure that x is monotonic increasing or monotonic decreasing.

In [119]:
data_scored = proba[:,1]
In [120]:
data_scoring["Score"]=data_scored
In [121]:
dataa=data_scoring
In [122]:
data_sorted = dataa.sort_values(by='Score', ascending = False)
data_sorted.head()
Out[122]:
PO59 PO47 SD1 PO82 PO78 PO81 SD37 SD43 PO44 SD22 PO57 PO61 SD18 SD35 PO76 SD32 PO65 SD7 SD30 SD24 SD38 SD28 SD26 SD15 SD14 SD23 PO80 PO79 SD39 SD10 PO60 CLASS Score
8443 4 6 12 1 0 0 0 7 2 7 0 3 1 7 0 7 1 5 0 2 5 1 3 5 2 1 1 0 4 5 0 1 0.972952
6397 5 6 8 2 0 0 0 7 0 0 0 4 2 2 0 3 0 2 0 0 2 0 0 4 4 0 1 0 5 7 0 1 0.968750
7320 3 6 1 0 1 0 0 8 2 2 3 0 0 0 0 5 1 7 0 1 2 0 2 5 4 0 1 0 6 7 0 1 0.962462
2188 4 6 7 1 0 0 0 6 2 7 0 4 0 7 0 7 1 7 0 0 6 0 3 3 6 0 1 0 3 7 0 0 0.959862
1595 3 6 38 1 0 0 2 4 0 3 0 4 4 9 0 7 0 5 7 2 2 5 2 4 3 3 1 0 5 4 0 0 0.958136
In [123]:
vised_clients = data_sorted.iloc[0:dataa.shape[0]//2, :]
vised_clients
Out[123]:
PO59 PO47 SD1 PO82 PO78 PO81 SD37 SD43 PO44 SD22 PO57 PO61 SD18 SD35 PO76 SD32 PO65 SD7 SD30 SD24 SD38 SD28 SD26 SD15 SD14 SD23 PO80 PO79 SD39 SD10 PO60 CLASS Score
8443 4 6 12 1 0 0 0 7 2 7 0 3 1 7 0 7 1 5 0 2 5 1 3 5 2 1 1 0 4 5 0 1 0.972952
6397 5 6 8 2 0 0 0 7 0 0 0 4 2 2 0 3 0 2 0 0 2 0 0 4 4 0 1 0 5 7 0 1 0.968750
7320 3 6 1 0 1 0 0 8 2 2 3 0 0 0 0 5 1 7 0 1 2 0 2 5 4 0 1 0 6 7 0 1 0.962462
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1595 3 6 38 1 0 0 2 4 0 3 0 4 4 9 0 7 0 5 7 2 2 5 2 4 3 3 1 0 5 4 0 0 0.958136
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2955 4 6 3 1 0 0 2 6 0 2 0 4 3 5 0 7 0 5 3 1 3 1 2 1 5 1 1 0 4 4 0 0 0.947659
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5853 0 6 9 0 0 0 4 4 2 4 0 0 2 6 0 5 1 2 8 4 3 3 2 4 5 2 0 0 0 6 0 0 0.139767
6412 0 0 33 0 0 0 0 3 0 3 0 0 6 9 0 7 0 4 0 3 2 5 2 7 2 3 0 0 7 9 0 0 0.139700
2707 0 0 39 0 0 0 2 5 0 3 0 0 6 9 0 6 0 3 6 2 7 6 1 6 2 5 0 0 1 8 0 0 0.139576
6510 0 5 39 0 0 0 2 5 0 3 0 0 6 9 0 6 0 3 6 2 7 6 1 6 2 5 0 0 1 8 0 0 0.139576
4335 3 0 37 0 0 0 5 4 0 4 0 0 6 7 0 4 0 5 4 2 2 4 2 3 4 3 1 0 2 6 0 0 0.139539
5825 3 5 9 0 0 0 2 4 2 5 0 0 4 7 0 7 1 3 3 2 5 2 1 4 4 1 1 0 3 5 0 0 0.139470
3277 0 0 33 0 0 0 3 3 2 4 0 0 7 9 0 7 1 4 0 3 6 4 0 7 2 0 0 0 0 7 0 0 0.139421
6402 0 0 33 0 0 0 3 3 2 4 0 0 7 9 0 7 1 4 0 3 6 4 0 7 2 0 0 0 0 7 0 0 0.139421
6936 0 0 9 0 0 0 3 4 2 3 0 0 5 3 0 5 1 7 1 0 4 3 2 3 3 2 0 0 4 7 0 0 0.139315
6449 4 0 33 0 0 0 3 3 0 2 0 0 6 7 0 6 0 5 4 2 5 5 1 4 3 4 1 0 2 5 0 0 0.139181
2788 3 6 34 0 0 0 2 6 0 1 0 0 6 7 0 4 0 3 5 2 5 3 2 3 5 3 1 0 1 5 0 0 0.139048
7409 0 0 11 0 0 0 2 6 0 4 0 0 3 6 0 6 0 4 3 2 3 2 2 4 3 1 0 0 4 6 0 0 0.139018
2309 0 6 8 0 0 0 6 7 0 6 0 0 3 2 0 9 0 6 9 0 0 2 1 5 0 0 0 0 2 6 0 0 0.138967
1095 0 6 8 0 0 0 6 7 0 6 0 0 3 2 0 9 0 6 9 0 0 2 1 5 0 0 0 0 2 6 0 0 0.138967
6328 0 6 8 0 0 0 6 7 0 6 0 0 3 2 0 9 0 6 9 0 0 2 1 5 0 0 0 0 2 6 0 0 0.138967
4255 0 0 35 0 0 0 1 5 2 3 0 0 5 1 0 7 1 2 4 3 4 3 2 4 5 2 0 0 4 8 0 0 0.138882
4125 4 6 33 0 0 0 1 3 2 1 0 0 8 7 0 5 1 1 9 3 6 5 1 8 1 6 1 0 3 7 0 0 0.138753
3245 4 0 39 0 0 0 1 5 1 4 0 0 6 5 0 5 1 6 2 3 4 3 2 8 0 0 1 0 2 6 0 0 0.138618
2738 4 0 35 0 0 0 2 5 2 3 0 0 5 8 0 5 1 5 2 1 5 3 3 3 3 4 1 0 2 3 0 0 0.138593
5549 4 0 35 0 0 0 2 5 2 3 0 0 5 8 0 5 1 5 2 1 5 3 3 3 3 4 1 0 2 3 0 0 0.138593
1961 3 0 13 0 0 0 0 7 0 5 0 0 2 7 0 5 0 3 4 3 5 2 3 4 3 1 1 0 3 6 0 0 0.138543
2742 0 6 27 0 0 0 2 1 2 2 0 0 9 9 0 7 1 4 9 3 7 3 0 4 2 3 0 0 0 5 0 0 0.138542
767 3 5 8 0 0 0 0 7 0 2 0 0 1 2 0 7 0 6 0 1 1 1 2 5 4 1 1 0 5 9 0 0 0.138538
1382 4 5 11 0 0 0 5 6 2 4 0 0 5 7 0 5 1 4 2 1 3 3 3 2 4 3 1 0 2 5 0 0 0.138443
656 3 6 37 0 0 0 2 4 0 3 0 0 6 6 0 7 0 6 2 1 2 4 2 6 3 4 1 0 6 8 0 0 0.138420
9164 6 0 33 0 0 0 0 3 0 4 0 0 6 7 0 7 0 6 2 4 4 5 2 5 3 2 1 0 5 6 0 0 0.138309
5055 0 5 3 0 0 0 2 6 0 3 0 0 3 6 0 7 0 6 3 1 4 3 1 6 3 2 0 0 3 8 0 0 0.138228
9345 0 0 9 0 0 0 4 4 2 4 0 0 3 6 0 4 1 3 9 3 3 4 3 7 2 1 0 0 2 7 0 0 0.138019
4701 0 0 9 0 0 0 4 4 2 4 0 0 3 6 0 4 1 3 9 3 3 4 3 7 2 1 0 0 2 7 0 0 0.138019
6207 1 0 38 0 0 0 1 4 0 3 0 0 6 7 0 6 0 4 5 3 4 6 1 7 2 3 1 0 4 9 0 0 0.137963
3081 3 6 32 0 0 0 3 1 2 3 0 0 6 6 1 5 1 3 2 3 4 4 2 4 2 2 1 0 2 5 0 0 0.137896

4911 rows × 33 columns

Pour maximiser le nombre des clients réceptifs AssurExperts Inc doit cibler les individus qui ont un score supérieur a 0.139975

In [124]:
FALSE_NEGATIVE=vised_clients.loc[(vised_clients['CLASS']==0)&(vised_clients['Score']>0.5)]
FALSE_NEGATIVE
Out[124]:
PO59 PO47 SD1 PO82 PO78 PO81 SD37 SD43 PO44 SD22 PO57 PO61 SD18 SD35 PO76 SD32 PO65 SD7 SD30 SD24 SD38 SD28 SD26 SD15 SD14 SD23 PO80 PO79 SD39 SD10 PO60 CLASS Score
2188 4 6 7 1 0 0 0 6 2 7 0 4 0 7 0 7 1 7 0 0 6 0 3 3 6 0 1 0 3 7 0 0 0.959862
1595 3 6 38 1 0 0 2 4 0 3 0 4 4 9 0 7 0 5 7 2 2 5 2 4 3 3 1 0 5 4 0 0 0.958136
2955 4 6 3 1 0 0 2 6 0 2 0 4 3 5 0 7 0 5 3 1 3 1 2 1 5 1 1 0 4 4 0 0 0.947659
5475 4 6 8 0 0 0 0 7 2 6 0 0 0 7 0 6 1 4 0 0 0 3 6 3 6 3 1 0 9 5 0 0 0.946674
7934 4 6 8 0 0 0 0 7 2 7 0 0 0 6 0 7 1 2 0 0 0 0 6 7 2 0 1 0 5 9 0 0 0.940261
7817 4 6 13 1 0 0 1 6 2 5 0 5 1 5 0 7 1 2 7 1 4 2 3 5 4 2 1 0 5 7 0 0 0.938439
8684 4 6 17 2 0 0 4 5 2 3 0 4 5 7 1 6 1 5 4 4 2 4 1 2 4 1 1 0 3 5 0 0 0.930802
8960 4 6 36 2 0 0 3 3 1 2 0 2 5 4 0 4 1 5 5 2 3 3 1 1 3 2 1 0 3 4 0 0 0.929577
1352 4 6 10 0 0 0 2 8 2 5 0 0 2 5 0 9 1 5 1 1 4 2 4 7 2 2 1 1 4 8 0 0 0.918962
4526 4 6 10 0 0 0 3 8 2 7 0 0 1 6 0 9 1 3 1 1 2 2 6 8 1 1 1 0 5 7 0 0 0.917394
2174 5 6 1 0 0 0 0 8 2 2 0 0 0 2 0 6 1 7 0 0 0 0 0 2 7 0 1 0 5 7 0 0 0.915282
1665 3 6 8 0 0 0 0 7 0 6 0 0 0 7 0 6 0 4 0 0 0 3 6 3 6 3 1 0 9 5 0 0 0.913704
8489 4 6 8 0 0 0 0 7 0 0 0 0 2 2 0 3 0 2 0 0 2 0 0 4 4 0 1 0 5 7 0 0 0.912148
2786 4 6 8 0 0 0 0 7 2 9 0 0 5 2 0 6 1 5 0 0 9 5 4 9 0 0 1 0 0 9 0 0 0.908097
8620 4 6 8 0 0 0 0 7 2 9 0 0 5 2 0 6 1 5 0 0 9 5 4 9 0 0 1 0 0 9 0 0 0.908097
2099 4 6 8 0 1 0 0 7 2 0 3 0 2 2 1 3 1 2 0 0 2 0 0 4 4 0 1 0 5 7 0 0 0.906925
5607 5 6 1 0 0 0 0 8 2 0 0 0 2 2 0 3 1 2 0 0 2 0 0 4 4 0 1 0 5 7 0 0 0.905649
4619 4 6 8 0 0 0 1 7 2 5 0 0 2 5 0 6 1 7 0 1 4 2 2 4 4 1 1 0 4 6 0 0 0.905498
4742 4 6 6 0 0 0 0 8 2 2 0 0 2 0 0 5 1 4 0 0 3 2 4 4 5 0 1 0 6 9 0 0 0.905323
4592 4 6 8 0 0 0 0 7 2 0 0 0 2 2 0 3 1 2 0 0 2 0 0 4 4 0 1 0 5 7 0 0 0.904050
1984 4 6 8 0 0 0 0 7 2 0 0 0 2 2 0 3 1 2 0 0 2 0 0 4 4 0 1 0 5 7 0 0 0.904050
482 4 6 8 0 0 0 0 7 2 6 0 0 4 9 0 9 1 3 2 0 0 4 2 9 0 2 1 0 4 9 0 0 0.902116
1499 4 6 8 0 0 0 0 7 0 9 0 0 5 2 0 6 0 5 0 0 9 5 4 9 0 0 1 0 0 9 0 0 0.901405
6766 4 6 1 0 0 0 0 8 2 2 0 0 0 0 0 5 1 7 0 1 2 0 2 5 4 0 1 0 6 7 0 0 0.897572
8570 4 6 1 0 0 0 0 8 2 2 0 0 0 0 0 5 1 7 0 1 2 0 2 5 4 0 1 0 6 7 0 0 0.897572
715 4 6 1 0 0 0 0 8 1 0 0 0 2 2 0 3 1 2 0 0 2 0 0 4 4 0 1 0 5 7 0 0 0.896361
4712 4 6 8 0 0 0 0 7 2 7 0 0 0 7 0 7 1 7 0 0 6 0 3 3 6 0 1 0 3 7 0 0 0.895466
6528 4 6 8 0 0 0 0 7 2 7 0 0 0 7 0 7 1 7 0 0 6 0 3 3 6 0 1 0 3 7 0 0 0.895466
2289 5 6 1 0 0 0 1 8 2 0 0 0 1 0 0 6 1 6 1 0 0 0 1 1 8 1 1 0 5 9 0 0 0.893538
7511 3 6 6 0 0 0 0 8 0 2 0 0 0 2 0 7 0 7 0 0 0 0 2 6 3 0 1 0 7 9 0 0 0.892740
865 3 6 2 0 0 0 2 6 2 0 0 0 0 3 0 7 1 5 6 5 4 4 0 0 9 0 1 0 4 9 0 0 0.892472
5092 4 6 6 0 0 0 0 8 0 1 0 0 1 5 0 7 0 5 0 3 1 1 2 6 3 1 1 0 3 6 0 0 0.888789
8905 4 6 1 0 0 0 1 8 2 1 0 0 1 5 0 5 1 5 1 1 1 1 3 1 8 1 1 0 3 5 0 0 0.886274
4061 3 6 8 0 0 0 0 7 0 9 0 0 5 2 0 6 0 5 0 0 9 5 4 9 0 0 1 0 0 9 0 0 0.885652
6869 4 6 41 0 0 0 0 4 2 0 0 0 0 5 0 6 1 4 4 0 0 0 0 4 4 0 1 0 9 7 0 0 0.885312
1078 4 6 35 0 0 0 2 5 2 2 0 0 2 5 2 6 1 7 1 2 3 2 2 5 2 1 1 0 4 7 0 0 0.885086
6596 4 6 12 0 0 0 2 7 2 6 0 0 2 4 0 9 1 3 0 2 3 1 2 5 2 0 1 0 5 5 0 0 0.882813
4036 4 6 8 0 0 0 0 7 2 4 0 0 4 5 0 8 1 7 0 1 6 4 2 7 2 2 1 0 3 9 0 0 0.881914
9169 4 6 8 0 0 0 0 7 2 4 0 0 4 5 0 8 1 7 0 1 6 4 2 7 2 2 1 0 3 9 0 0 0.881914
4807 4 6 8 0 0 0 2 7 2 2 0 0 1 3 0 7 1 4 6 1 4 2 0 2 6 0 1 0 4 7 0 0 0.880163
927 4 6 8 0 0 0 2 7 2 2 0 0 1 3 0 7 1 4 6 1 4 2 0 2 6 0 1 0 4 7 0 0 0.880163
2153 4 6 8 0 0 0 2 7 2 2 0 0 1 3 0 7 1 4 6 1 4 2 0 2 6 0 1 0 4 7 0 0 0.880163
502 4 6 33 0 0 0 2 3 2 2 0 0 4 5 2 7 1 5 2 3 4 3 1 4 3 2 1 0 3 7 0 0 0.878442
7173 4 6 10 0 0 0 3 8 2 5 0 0 3 5 0 9 1 3 1 1 2 3 3 8 1 3 1 0 4 8 0 0 0.878202
156 4 6 10 0 0 0 3 8 2 5 0 0 3 5 0 9 1 3 1 1 2 3 3 8 1 3 1 0 4 8 0 0 0.878202
5244 4 6 10 0 0 0 3 8 2 5 0 0 3 5 0 9 1 3 1 1 2 3 3 8 1 3 1 0 4 8 0 0 0.878202
2506 0 6 38 0 0 0 0 4 0 2 0 0 7 7 0 9 0 6 2 0 9 9 0 5 2 7 0 0 0 5 0 0 0.877877
3656 0 6 38 0 0 0 0 4 0 2 0 0 7 7 0 9 0 6 2 0 9 9 0 5 2 7 0 0 0 5 0 0 0.877877
457 0 0 38 0 0 0 0 4 0 2 0 0 7 7 0 9 0 6 2 0 9 9 0 5 2 7 0 0 0 5 0 0 0.877650
7104 0 0 38 0 0 0 0 4 0 2 0 0 7 7 0 9 0 6 2 0 9 9 0 5 2 7 0 0 0 5 0 0 0.877650
496 4 6 8 0 0 0 1 7 2 4 0 0 2 5 1 7 1 5 1 1 3 3 3 7 2 3 1 0 4 7 0 0 0.877033
3525 4 6 1 0 0 0 2 8 2 0 0 0 2 2 0 9 1 2 0 0 2 1 0 2 3 2 1 0 5 6 0 0 0.875955
7752 4 6 1 0 0 0 2 8 2 0 0 0 2 2 0 9 1 2 0 0 2 1 0 2 3 2 1 0 5 6 0 0 0.875955
3943 4 6 1 0 0 0 2 8 2 0 0 0 2 2 0 9 1 2 0 0 2 1 0 2 3 2 1 0 5 6 0 0 0.875955
6394 3 6 37 0 0 0 3 4 2 0 0 0 4 7 0 3 1 7 2 2 4 5 0 4 2 3 1 0 2 6 0 0 0.874959
7718 4 6 6 0 0 0 0 8 2 2 0 0 2 2 1 7 1 4 0 1 2 2 2 5 4 2 1 0 3 9 0 0 0.870969
6113 4 6 33 1 0 0 5 3 2 2 0 5 9 9 0 7 1 2 9 4 4 7 0 3 6 4 1 0 0 9 0 0 0.870673
182 4 6 10 0 0 0 2 8 2 4 0 0 5 5 0 7 1 5 3 0 7 5 0 6 3 5 1 0 0 9 0 0 0.870463
7869 0 0 8 1 0 0 0 7 0 0 0 1 2 2 0 3 0 2 0 0 2 0 0 4 4 0 0 0 5 7 0 0 0.870443
1933 3 6 1 0 0 0 1 8 2 1 0 0 1 2 0 5 1 5 1 1 1 1 1 8 1 1 1 0 7 8 0 0 0.870285
2612 4 6 8 0 0 0 0 7 0 7 0 0 0 7 0 7 0 7 0 0 6 0 3 3 6 0 1 0 3 7 0 0 0.869930
7205 4 6 8 0 0 0 0 7 0 7 0 0 0 7 0 7 0 7 0 0 6 0 3 3 6 0 1 0 3 7 0 0 0.869930
4872 4 6 8 0 0 0 0 7 0 7 0 0 0 7 0 7 0 7 0 0 6 0 3 3 6 0 1 0 3 7 0 0 0.869930
3940 4 6 8 0 0 0 0 7 0 7 0 0 0 7 0 7 0 7 0 0 6 0 3 3 6 0 1 0 3 7 0 0 0.869930
5172 4 6 1 0 0 0 0 8 2 2 0 0 1 5 0 7 1 5 0 1 1 1 3 7 2 2 1 0 4 6 0 0 0.869580
4913 0 6 8 1 0 0 0 7 0 9 0 3 5 2 0 6 0 5 0 0 9 5 4 9 0 0 0 0 0 9 0 0 0.868458
3048 4 6 10 0 0 0 1 8 2 4 0 0 2 6 0 7 1 2 0 0 5 2 4 7 2 2 1 0 3 7 0 0 0.868127
5619 3 6 36 1 0 0 2 3 2 3 0 2 6 6 0 5 1 6 5 4 5 6 1 4 4 2 1 0 2 7 0 0 0.868115
996 3 6 1 0 0 0 2 8 2 0 0 0 2 2 0 9 1 2 0 0 2 1 0 2 3 2 1 0 5 6 0 0 0.867786
7206 3 6 1 0 0 0 2 8 2 0 0 0 2 2 0 9 1 2 0 0 2 1 0 2 3 2 1 0 5 6 0 0 0.867786
9658 4 6 8 0 0 0 2 7 2 4 0 0 3 6 0 7 1 5 3 2 0 4 2 6 3 2 1 0 7 9 0 0 0.867241
3887 3 6 8 0 0 0 2 7 2 2 0 0 1 3 0 7 1 4 6 1 4 2 0 2 6 0 1 0 4 7 0 0 0.866569
2593 4 6 34 1 0 0 0 6 2 2 0 3 7 9 0 5 1 7 2 2 5 5 0 5 4 4 1 0 4 7 0 0 0.864745
3031 4 6 7 0 0 0 1 6 2 4 0 0 1 4 0 7 1 5 0 0 3 1 4 8 1 1 1 0 6 8 0 0 0.864100
8686 4 6 39 0 0 0 4 5 2 3 0 0 4 7 2 7 1 7 0 0 2 5 2 4 2 4 1 0 4 6 0 0 0.863397
1725 4 6 3 0 0 0 3 6 2 2 0 0 0 5 0 5 1 5 0 1 2 5 4 5 4 1 1 0 3 5 0 0 0.858838
4371 4 6 1 0 0 0 0 8 2 2 0 0 3 5 0 6 1 5 0 0 1 3 1 3 2 0 1 0 4 9 0 0 0.858442
6917 3 6 36 0 0 0 4 3 1 0 0 0 9 9 0 5 1 4 9 4 4 7 0 2 7 5 1 0 2 9 0 0 0.856713
784 4 6 1 0 0 0 1 8 2 2 0 0 2 3 0 6 1 4 0 1 3 2 2 4 4 1 1 0 4 7 0 0 0.854575
1932 4 6 1 0 0 0 1 8 2 2 0 0 2 3 0 6 1 4 0 1 3 2 2 4 4 1 1 0 4 7 0 0 0.854575
7090 4 6 1 0 0 0 1 8 2 2 0 0 2 3 0 6 1 4 0 1 3 2 2 4 4 1 1 0 4 7 0 0 0.854575
4587 4 6 7 0 0 0 1 6 0 4 0 0 1 4 0 7 0 5 0 0 3 1 4 8 1 1 1 0 6 8 0 0 0.852517
920 0 6 33 0 0 0 7 3 0 1 0 0 8 7 0 5 0 1 9 1 2 8 1 3 3 8 0 0 1 6 0 0 0.851360
4073 0 6 33 0 0 0 7 3 0 1 0 0 8 7 0 5 0 1 9 1 2 8 1 3 3 8 0 0 1 6 0 0 0.851360
7215 0 6 33 0 0 0 7 3 0 1 0 0 8 7 0 5 0 1 9 1 2 8 1 3 3 8 0 0 1 6 0 0 0.851360
7641 4 6 9 0 0 0 1 4 2 5 0 0 2 5 0 6 1 7 0 1 4 2 2 4 4 1 1 0 4 6 0 0 0.847089
7074 4 6 12 0 0 0 2 7 2 5 0 0 4 5 0 9 1 6 0 1 4 4 3 6 2 2 1 0 4 7 0 0 0.846254
7913 4 6 12 0 0 0 2 7 2 5 0 0 4 5 0 9 1 6 0 1 4 4 3 6 2 2 1 0 4 7 0 0 0.846254
6517 3 6 33 0 0 0 4 3 2 3 0 0 6 7 0 7 1 7 4 2 3 7 0 6 3 3 1 0 3 8 0 0 0.846166
8481 3 6 39 0 0 0 2 5 0 0 0 0 4 5 0 7 0 7 1 4 3 2 0 7 2 2 1 0 2 7 0 0 0.845608
5865 4 6 35 0 0 0 2 5 2 0 0 0 2 2 0 9 1 2 0 0 2 1 0 2 3 2 1 0 5 6 0 0 0.845553
36 4 6 7 0 0 0 0 6 2 7 0 0 0 7 0 7 1 7 0 0 6 0 3 3 6 0 1 0 3 7 0 0 0.842979
1327 0 6 33 0 1 0 3 3 2 2 2 0 9 9 0 7 1 7 9 0 4 9 0 5 4 7 0 0 2 7 0 0 0.842746
8222 0 6 10 0 0 0 6 8 0 4 0 0 5 6 0 7 0 2 9 3 0 3 0 5 0 1 0 0 3 7 0 0 0.842512
427 4 0 12 0 1 0 2 7 2 7 2 0 6 9 0 9 1 2 0 2 0 6 2 9 0 0 1 0 5 9 0 0 0.841989
9622 4 6 8 0 0 0 1 7 2 5 0 0 4 8 0 7 1 4 1 1 6 4 3 6 1 2 1 0 3 6 0 0 0.840605
430 3 6 33 0 0 0 2 3 2 1 0 0 7 8 0 6 1 7 6 6 6 5 0 5 1 2 1 0 0 5 0 0 0.840568
4817 3 6 33 0 0 0 2 3 2 1 0 0 7 8 0 6 1 7 6 6 6 5 0 5 1 2 1 0 0 5 0 0 0.840568
1481 3 6 33 0 0 0 2 3 2 1 0 0 7 8 0 6 1 7 6 6 6 5 0 5 1 2 1 0 0 5 0 0 0.840568
1588 3 6 8 0 0 0 0 7 2 7 0 0 0 7 0 7 1 7 0 0 6 0 3 3 6 0 1 0 3 7 0 0 0.840103
9304 4 6 8 0 0 0 2 7 2 2 0 0 2 4 0 9 1 4 0 2 2 1 2 4 5 1 1 0 5 8 0 0 0.837948
1146 4 6 8 0 0 0 2 7 2 2 0 0 2 4 0 9 1 4 0 2 2 1 2 4 5 1 1 0 5 8 0 0 0.837948
5054 5 6 3 0 0 0 3 6 2 4 0 0 2 2 0 6 1 5 6 0 4 2 0 2 5 0 1 0 3 7 0 0 0.837931
9239 4 6 33 0 0 0 4 3 2 3 0 0 6 7 0 7 1 7 4 2 3 7 0 6 3 3 1 0 3 8 0 0 0.837666
4808 4 6 33 0 0 0 4 3 2 3 0 0 6 7 0 7 1 7 4 2 3 7 0 6 3 3 1 0 3 8 0 0 0.837666
7004 4 6 39 0 0 0 4 5 2 3 0 0 4 7 0 7 1 7 0 0 2 5 2 4 2 4 1 0 4 6 0 0 0.837511
2434 3 6 23 0 0 0 4 3 2 3 0 0 4 7 0 6 1 4 8 0 0 7 1 1 4 4 1 0 5 4 0 0 0.837104
461 4 6 35 0 0 0 2 5 2 2 0 0 2 5 0 6 1 7 1 2 3 2 2 5 2 1 1 0 4 7 0 0 0.836161
5101 4 6 3 0 0 0 0 6 2 2 0 0 3 7 0 7 1 5 4 3 4 2 0 3 4 0 1 0 5 5 0 0 0.835582
534 4 6 3 0 0 0 0 6 2 2 0 0 3 7 0 7 1 5 4 3 4 2 0 3 4 0 1 0 5 5 0 0 0.835582
508 5 6 33 0 0 0 1 3 2 1 0 0 4 9 0 5 1 9 1 1 4 8 0 4 5 8 2 0 0 9 0 0 0.831167
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8829 4 6 6 0 0 0 0 8 2 2 0 0 1 3 2 8 1 6 0 1 3 2 2 8 1 2 1 0 5 9 0 0 0.830205
5326 3 6 1 0 0 0 0 8 0 1 0 0 1 5 0 7 0 5 0 4 1 1 1 5 4 1 1 0 5 6 0 0 0.829590
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9028 3 6 38 0 0 0 2 4 2 4 0 0 4 6 0 7 1 6 9 1 5 4 3 5 4 2 1 0 3 9 0 0 0.828086
17 3 6 22 0 0 0 0 2 2 1 0 0 7 7 0 6 1 5 4 5 6 7 0 7 2 1 1 0 3 7 0 0 0.827731
2029 4 6 13 0 0 0 1 6 2 6 0 0 1 6 0 7 1 4 7 1 5 2 2 6 3 1 1 0 4 7 0 0 0.825382
5106 4 6 10 0 0 0 3 8 2 5 0 0 3 5 1 9 1 3 1 1 2 3 3 8 1 3 1 0 4 8 0 0 0.824711
3393 4 6 38 0 0 0 4 4 2 3 0 0 5 8 0 6 1 5 6 3 3 3 2 3 4 2 1 0 3 4 0 0 0.823870
3094 4 6 6 0 0 0 0 8 2 7 0 0 0 7 0 7 1 7 0 0 6 0 3 3 6 0 1 0 3 7 0 0 0.823803
2147 4 6 6 0 0 0 0 8 2 7 0 0 0 7 0 7 1 7 0 0 6 0 3 3 6 0 1 0 3 7 0 0 0.823803
2179 0 6 8 0 0 0 0 7 0 0 0 0 2 2 0 3 0 2 0 0 2 0 0 4 4 0 0 0 5 7 0 0 0.821934
406 4 6 11 0 0 0 2 6 2 4 0 0 4 7 0 7 1 7 1 1 4 2 2 4 3 0 2 0 3 9 0 0 0.821728
6922 3 6 8 0 0 0 1 7 0 4 0 0 2 5 0 7 0 5 1 1 3 3 3 7 2 3 1 0 4 7 0 0 0.821338
4929 3 6 8 0 0 0 1 7 0 4 0 0 2 5 0 7 0 5 1 1 3 3 3 7 2 3 1 0 4 7 0 0 0.821338
4216 4 6 33 0 0 0 0 3 2 0 0 0 2 2 0 9 1 5 0 2 2 2 0 3 6 0 1 0 5 9 0 0 0.821052
8284 4 6 3 0 0 0 2 6 0 4 0 0 3 7 0 6 0 4 6 1 3 3 3 2 5 2 1 0 3 5 0 0 0.820938
3210 3 6 39 0 0 0 0 5 2 3 0 0 4 4 0 6 1 5 0 1 4 2 1 4 3 2 1 0 4 6 0 0 0.820379
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4078 4 5 10 0 0 0 2 8 2 5 0 0 2 5 1 9 1 5 1 1 4 2 4 7 2 2 1 0 4 8 0 0 0.819799
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3431 4 6 13 0 0 0 0 6 2 7 0 0 0 7 0 7 1 7 0 0 6 0 3 3 6 0 1 0 3 7 0 0 0.818655
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1998 4 6 1 0 0 0 2 8 0 3 0 0 1 2 0 7 0 5 0 1 1 0 1 6 2 0 1 0 6 7 0 0 0.816189
65 4 6 33 0 0 0 0 3 2 4 0 0 4 6 0 7 1 8 0 1 6 4 2 7 2 1 1 0 3 9 0 0 0.814581
2270 0 0 7 1 0 0 0 6 0 7 0 2 0 7 0 7 0 7 0 0 6 0 3 3 6 0 0 0 3 7 0 0 0.814132
26 0 6 37 0 0 0 2 4 0 5 0 0 2 5 0 9 0 5 8 2 3 2 3 6 3 2 0 0 3 7 0 0 0.813935
3654 0 6 37 0 0 0 2 4 0 5 0 0 2 5 0 9 0 5 8 2 3 2 3 6 3 2 0 0 3 7 0 0 0.813935
6291 4 6 37 0 0 0 5 4 2 9 0 0 6 5 0 6 1 4 7 0 3 5 3 2 4 0 1 0 1 7 0 0 0.812599
2157 4 6 36 0 0 0 0 3 2 4 0 0 4 7 0 7 1 7 4 0 3 5 2 4 3 4 1 0 4 6 0 0 0.812254
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5174 4 6 33 0 0 0 2 3 2 4 0 0 6 9 0 6 1 4 3 3 5 6 1 7 2 3 1 0 2 8 0 0 0.806514
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1199 4 6 33 0 0 0 2 3 2 4 0 0 6 9 0 6 1 4 3 3 5 6 1 7 2 3 1 0 2 8 0 0 0.806514
568 4 6 33 0 0 0 2 3 2 4 0 0 6 9 0 6 1 4 3 3 5 6 1 7 2 3 1 0 2 8 0 0 0.806514
3861 4 6 3 0 0 0 1 6 2 2 0 0 4 5 0 5 1 5 2 1 2 4 1 2 6 4 1 0 4 5 0 0 0.805931
7632 4 6 3 0 0 0 1 6 2 2 0 0 4 5 0 5 1 5 2 1 2 4 1 2 6 4 1 0 4 5 0 0 0.805931
2989 4 6 3 0 0 0 1 6 2 2 0 0 4 5 0 5 1 5 2 1 2 4 1 2 6 4 1 0 4 5 0 0 0.805931
4965 4 6 3 0 0 0 2 6 2 4 0 0 3 7 0 6 1 4 6 1 3 3 3 2 5 2 1 0 3 5 0 0 0.805728
2307 4 6 39 0 0 0 2 5 2 3 0 0 5 6 0 6 1 5 3 3 4 4 3 6 2 2 1 0 3 7 0 0 0.805054
8190 4 6 39 0 0 0 2 5 2 3 0 0 5 6 0 6 1 5 3 3 4 4 3 6 2 2 1 0 3 7 0 0 0.805054
6107 4 6 7 0 1 0 1 6 2 4 2 0 1 4 1 7 1 5 0 0 3 1 4 8 1 1 1 0 6 8 0 0 0.803899
369 4 6 33 0 0 0 2 3 2 2 0 0 4 5 0 7 1 5 2 3 4 3 1 4 3 2 1 0 3 7 0 0 0.803893
7686 3 6 24 0 0 0 2 2 2 6 0 0 2 8 0 4 1 2 8 1 5 2 4 2 4 1 1 0 1 5 0 0 0.803855
6388 4 6 9 0 0 0 0 4 2 4 0 0 3 8 1 6 1 4 0 3 6 3 0 5 4 2 1 0 0 7 0 0 0.803724
810 4 6 1 0 0 0 0 8 0 2 0 0 3 5 0 6 0 5 0 0 1 3 1 3 2 0 1 0 4 9 0 0 0.802936
1776 3 6 38 0 0 0 2 4 2 3 0 0 3 6 0 7 1 6 3 1 4 3 1 6 3 2 1 0 3 8 0 0 0.802632
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1362 rows × 33 columns

In [125]:
FALSE_NEGATIVE.shape[0]/dataa.shape[0]*100
Out[125]:
13.866829566279781

Le pourcentage des clients qui sont désintéressé par La police d’assurance sur caravane,mais qui sont suceptibles d'etre pour est de 13.86% parmi les clients active

Evaluation

Lorsque nous sommes arrivés au but, nous sommes passés à la révision comme étant une étape primordiale de l'évaluation alors on a remarqué qu'il y a des clients qui ont répondu par "NON" et il s'est avéré qu’ils peuvent être intéressés. De même, il y a ceux qui ont indiqué qu’ils ont un intérêt alors qu'ils ne sont pas forcément intéressés.

faut-il donc envoyer des mails uniquement aux clients qui ont répondu par 'OUI' au sondage?

Deploiement

L’une des questions à envisager est celle de cibler les clients qui ont répondu par 'NON', mais qui peuvent être intéresses d'après notre modèle.

Dans ce cas, on peut trouver une solution pour que la compagnie d'assurance ne gaspillera plus d'argent en limitant le nombre de mails envoyés.